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Dissecting Medical Referral Mechanisms in Health Services: Role of Physician Professional Networks
Published 4 Dec 2023 in cs.CY, cs.LG, and cs.SI | (2312.02387v1)
Abstract: Medical referrals between primary care physicians (PC) and specialist care (SC) physicians profoundly impact patient care regarding quality, satisfaction, and cost. This paper investigates the influence of professional networks among medical doctors on referring patients from PC to SC. Using five-year consultation data from a Portuguese private health provider, we conducted exploratory data analysis and constructed both professional and referral networks among physicians. We then apply Graph Neural Network (GNN) models to learn latent representations of the referral network. Our analysis supports the hypothesis that doctors' professional social connections can predict medical referrals, potentially enhancing collaboration within organizations and improving healthcare services. This research contributes to dissecting the underlying mechanisms in primary-specialty referrals, thereby providing valuable insights for enhancing patient care and effective healthcare management.
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[2013] Chan, T., Sabir, K., Sanhan, S., Sherbino, J.: Understanding the impact of residents’ interpersonal relationships during emergency department referrals and consultations. Journal of graduate medical education 5(4), 576 (2013) Berendsen et al. [2007] Berendsen, A.J., Benneker, W.H., Meyboom-de Jong, B., Klazinga, N.S., Schuling, J.: Motives and preferences of general practitioners for new collaboration models with medical specialists: a qualitative study. BMC Health Services Research 7(1), 1–9 (2007) Ekwegh and Dean [2020] Ekwegh, U., Dean, J.: Improving care planning and communication for frail older persons across the primary–secondary care interface. Future Healthcare Journal 7(3), 23 (2020) Appel et al. [2018] Appel, A.P., Santana, V.F., Moyano, L.G., Ito, M., Pinhanez, C.S.: A social network analysis framework for modeling health insurance claims data. arXiv preprint arXiv:1802.07116 (2018) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Son and Kim [2021] Son, J., Kim, D.: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PloS one 16(4), 0249404 (2021) Xiao and Deng [2020] Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Christakis, N.A., O’Malley, A.J., Onnela, J.P., Keating, N.L., Landon, B.E.: Physician Patient-Sharing Networks and the Cost and Intensity of Care in US Hospitals. Medical Care 50(2), 152–160 (2012) Shortell and Anderson [1971] Shortell, S.M., Anderson, O.W.: The physician referral process: a theoretical perspective. Health services research 6(1), 39 (1971) Muzzin [1992] Muzzin, L.J.: Understanding the process of medical referral: part 5: communication. Canadian Family Physician 38, 301 (1992) Chan et al. [2013] Chan, T., Sabir, K., Sanhan, S., Sherbino, J.: Understanding the impact of residents’ interpersonal relationships during emergency department referrals and consultations. Journal of graduate medical education 5(4), 576 (2013) Berendsen et al. [2007] Berendsen, A.J., Benneker, W.H., Meyboom-de Jong, B., Klazinga, N.S., Schuling, J.: Motives and preferences of general practitioners for new collaboration models with medical specialists: a qualitative study. BMC Health Services Research 7(1), 1–9 (2007) Ekwegh and Dean [2020] Ekwegh, U., Dean, J.: Improving care planning and communication for frail older persons across the primary–secondary care interface. Future Healthcare Journal 7(3), 23 (2020) Appel et al. [2018] Appel, A.P., Santana, V.F., Moyano, L.G., Ito, M., Pinhanez, C.S.: A social network analysis framework for modeling health insurance claims data. arXiv preprint arXiv:1802.07116 (2018) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Son and Kim [2021] Son, J., Kim, D.: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PloS one 16(4), 0249404 (2021) Xiao and Deng [2020] Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Shortell, S.M., Anderson, O.W.: The physician referral process: a theoretical perspective. Health services research 6(1), 39 (1971) Muzzin [1992] Muzzin, L.J.: Understanding the process of medical referral: part 5: communication. Canadian Family Physician 38, 301 (1992) Chan et al. [2013] Chan, T., Sabir, K., Sanhan, S., Sherbino, J.: Understanding the impact of residents’ interpersonal relationships during emergency department referrals and consultations. Journal of graduate medical education 5(4), 576 (2013) Berendsen et al. [2007] Berendsen, A.J., Benneker, W.H., Meyboom-de Jong, B., Klazinga, N.S., Schuling, J.: Motives and preferences of general practitioners for new collaboration models with medical specialists: a qualitative study. BMC Health Services Research 7(1), 1–9 (2007) Ekwegh and Dean [2020] Ekwegh, U., Dean, J.: Improving care planning and communication for frail older persons across the primary–secondary care interface. Future Healthcare Journal 7(3), 23 (2020) Appel et al. [2018] Appel, A.P., Santana, V.F., Moyano, L.G., Ito, M., Pinhanez, C.S.: A social network analysis framework for modeling health insurance claims data. arXiv preprint arXiv:1802.07116 (2018) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Son and Kim [2021] Son, J., Kim, D.: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Muzzin, L.J.: Understanding the process of medical referral: part 5: communication. Canadian Family Physician 38, 301 (1992) Chan et al. 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[2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Son and Kim [2021] Son, J., Kim, D.: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PloS one 16(4), 0249404 (2021) Xiao and Deng [2020] Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. 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[2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Chan, T., Sabir, K., Sanhan, S., Sherbino, J.: Understanding the impact of residents’ interpersonal relationships during emergency department referrals and consultations. Journal of graduate medical education 5(4), 576 (2013) Berendsen et al. [2007] Berendsen, A.J., Benneker, W.H., Meyboom-de Jong, B., Klazinga, N.S., Schuling, J.: Motives and preferences of general practitioners for new collaboration models with medical specialists: a qualitative study. BMC Health Services Research 7(1), 1–9 (2007) Ekwegh and Dean [2020] Ekwegh, U., Dean, J.: Improving care planning and communication for frail older persons across the primary–secondary care interface. Future Healthcare Journal 7(3), 23 (2020) Appel et al. 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[2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Son, J., Kim, D.: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PloS one 16(4), 0249404 (2021) Xiao and Deng [2020] Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. 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[2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. 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[2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. 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[2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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[2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 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[2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. 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[2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Muzzin, L.J.: Understanding the process of medical referral: part 5: communication. Canadian Family Physician 38, 301 (1992) Chan et al. [2013] Chan, T., Sabir, K., Sanhan, S., Sherbino, J.: Understanding the impact of residents’ interpersonal relationships during emergency department referrals and consultations. Journal of graduate medical education 5(4), 576 (2013) Berendsen et al. [2007] Berendsen, A.J., Benneker, W.H., Meyboom-de Jong, B., Klazinga, N.S., Schuling, J.: Motives and preferences of general practitioners for new collaboration models with medical specialists: a qualitative study. BMC Health Services Research 7(1), 1–9 (2007) Ekwegh and Dean [2020] Ekwegh, U., Dean, J.: Improving care planning and communication for frail older persons across the primary–secondary care interface. Future Healthcare Journal 7(3), 23 (2020) Appel et al. [2018] Appel, A.P., Santana, V.F., Moyano, L.G., Ito, M., Pinhanez, C.S.: A social network analysis framework for modeling health insurance claims data. arXiv preprint arXiv:1802.07116 (2018) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Son and Kim [2021] Son, J., Kim, D.: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PloS one 16(4), 0249404 (2021) Xiao and Deng [2020] Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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[2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Son, J., Kim, D.: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PloS one 16(4), 0249404 (2021) Xiao and Deng [2020] Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. 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[2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. 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[2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Chan, T., Sabir, K., Sanhan, S., Sherbino, J.: Understanding the impact of residents’ interpersonal relationships during emergency department referrals and consultations. Journal of graduate medical education 5(4), 576 (2013) Berendsen et al. [2007] Berendsen, A.J., Benneker, W.H., Meyboom-de Jong, B., Klazinga, N.S., Schuling, J.: Motives and preferences of general practitioners for new collaboration models with medical specialists: a qualitative study. BMC Health Services Research 7(1), 1–9 (2007) Ekwegh and Dean [2020] Ekwegh, U., Dean, J.: Improving care planning and communication for frail older persons across the primary–secondary care interface. Future Healthcare Journal 7(3), 23 (2020) Appel et al. 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In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. 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[2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. 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[2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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[2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. 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[2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. 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Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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[2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. 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[2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. 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International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. 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In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. 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JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. 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[2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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[2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. 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[2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Son and Kim [2021] Son, J., Kim, D.: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PloS one 16(4), 0249404 (2021) Xiao and Deng [2020] Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Son, J., Kim, D.: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PloS one 16(4), 0249404 (2021) Xiao and Deng [2020] Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. 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[2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. 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[2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. 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[2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. 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In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. 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[2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. 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In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. 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In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Son and Kim [2021] Son, J., Kim, D.: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PloS one 16(4), 0249404 (2021) Xiao and Deng [2020] Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. 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[2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Son, J., Kim, D.: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PloS one 16(4), 0249404 (2021) Xiao and Deng [2020] Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. 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[2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. 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[2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. 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[2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Son, J., Kim, D.: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PloS one 16(4), 0249404 (2021) Xiao and Deng [2020] Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. 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[2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. 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[2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. 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Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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[2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Son and Kim [2021] Son, J., Kim, D.: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PloS one 16(4), 0249404 (2021) Xiao and Deng [2020] Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Son, J., Kim, D.: Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities. PloS one 16(4), 0249404 (2021) Xiao and Deng [2020] Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. 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[2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. 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[2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. 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[2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. 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In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. 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Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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[2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one 15(9), 0238915 (2020) Choi et al. [2021] Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.-k.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos one 16(8), 0256039 (2021) Li and Jung [2021] Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. [2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, G., Jung, J.J.: Dynamic graph embedding for outlier detection on multiple meteorological time series. Plos one 16(2), 0247119 (2021) An et al. 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[2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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[2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. 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[2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. 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Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? 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[2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. 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In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. 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[2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. 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[2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. 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Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. 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[2018] An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. 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[2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley, A.J., Rockmore, D.N.: Referral paths in the U.S. physician network. Applied Network Science 3(1) (2018) Almansoori et al. [2012] Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. 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[2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. 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[2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. 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[2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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[2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 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Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Almansoori, W., Murshid, A., Xylogiannopoulos, K.F., Alhajj, R., Rokne, J.: Electronic medical referral system: Decision support and recommendation approach. In: 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI), pp. 572–577 (2012). IEEE Han et al. [2018] Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Han, Q., Ji, M., Martinez de Rituerto de Troya, I., Gaur, M., Zejnilovic, L.: A hybrid recommender system for patient-doctor matchmaking in primary care. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 481–490 (2018) Barnett et al. [2011] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. 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[2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. 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[2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. 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[2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. 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JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. 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[2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. 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Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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[2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2011) Kinchen et al. [2004] Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. 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[2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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[2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 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[2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. 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[2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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[2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020)
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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kinchen, K.S., Cooper, L.A., Levine, D., Wang, N.Y., Powe, N.R.: Referral of patients to specialists: factors affecting choice of specialist by Primary Care Physicians. Annals of Family Medicine 2(3), 245 (2004) Landon et al. [2012] Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. 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International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. 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In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. 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SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Barnett, M.L., Onnela, J., Paul, S., O’Malley, A.J., Keegan, T., Christakis, N.A.: Variation in patient-sharing networks of physicians across the united states. Journal of American Medical Association 308(3), 265–274 (2012) Yao et al. [2021] Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yao, Z., Yang, D., Levine, J.M., Low, C.A., Smith, T., Zhu, H., Kraut, R.E.: Join, stay or go? a closer look at members’ life cycles in online health communities. In: Proceedings of the ACM on Human-Computer Interaction, pp. 1–22 (2021) Barnett et al. [2012] Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. 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In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. 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International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. 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In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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[2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. 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Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. 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IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020)
- Barnett, M.L., Keating, N.L., Christakis, N.A., O’Malley, A.J., Landon, B.E.: Reasons for choice of referral physician among primary care and specialist physicians. Journal of General Internal Medicine 27(5), 506–512 (2012) https://doi.org/10.1007/s11606-011-1861-z Frank et al. [2000] Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Frank, P., Williams, G.C., Zwanziger, J., Mooney, C., Sorbero, M.: Why do physicians vary so widely in their referral rates? Journal of General Internal Medicine 15(2), 163–168 (2000) Barnett et al. [2012] Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. 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[2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Barnett, M.L., Song, Z., Landon, B.E.: Trends in Physician Referrals in the US, 1999–2009. Archives of internal medicine 172(2), 163–170 (2012) https://doi.org/10.1001/archinternmed.2011.722 O’Malley and Reschovsky [2011] O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. 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[2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. 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Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) O’Malley, A., Reschovsky, J.D.: Referral and consultation communication between primary care and specialist physicians. Archives of Internal Medicine 171(1), 56–66 (2011) Zuchowski et al. [2014] Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zuchowski, J.L., Rose, D.E., Hamilton, A.B., Stockdale, S.E., Meredith, L.S., Yano, E.M., Rubenstein, L.V., Cordasco, K.M.: Challenges in referral communication between vha primary care and specialty care. Journal of General Internal Medicine 30(3), 305–311 (2014) Rajpurkar et al. [2022] Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rajpurkar, P., Chen, E., Banerjee, O., Topol, E.J.: Ai in health and medicine. Nature Medicine 28, 31–38 (2022) Aboueid et al. [2019] Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Aboueid, S., Liu, R.H., Desta, B.N., Chaurasia, A., Ebrahim, S.: The use of artificially intelligent self-diagnosing digital platforms by the general public: Scoping review. JMIR Medical Informatics 7(2), 13455 (2019) Jagannatha and Yu [2016] Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Jagannatha, A.N., Yu, H.: Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter, pp. 473–482 (2016) Ravì et al. [2016] Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. 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[2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. 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KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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[2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. 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[2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. 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[2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. 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Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE journal of biomedical and health informatics 21(1), 4–21 (2016) Esteva et al. [2019] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., Dean, J.: A guide to deep learning in healthcare. Nature medicine 25(1), 24–29 (2019) Rotmensch et al. [2017] Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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[2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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[2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. 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[2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. 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SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. 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Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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[2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. 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SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. 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Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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[2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Scientific reports 7(1), 1–11 (2017) Kohler et al. [2022] Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. 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[2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kohler, K., Jankowski, M.D., Bashford, T., Goyal, D.G., Habermann, E.B., Walker, L.E.: Using network analysis to model the effects of the sars cov2 pandemic on acute patient care within a healthcare system. Scientific reports 12(1), 1–8 (2022) Wang et al. [2019] Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Wang, L., Wang, H., Song, Y., Wang, Q.: Mcpl-based ft-lstm: medical representation learning-based clinical prediction model for time series events. IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. 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Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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IEEE Access 7, 70253–70264 (2019) Song et al. [2020] Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020)
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[2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Song, J., Wang, Y., Tang, S., Zhang, Y., Chen, Z., Zhang, Z., Zhang, T., Wu, F.: Local–global memory neural network for medication prediction. IEEE Transactions on Neural Networks and Learning Systems 32(4), 1723–1736 (2020) Kodialam et al. [2020] Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Kodialam, R.S., Boiarsky, R., Lim, J., Dixit, N., Sai, A., Sontag, D.: Deep contextual clinical prediction with reverse distillation. arXiv preprint arXiv:2007.05611 (2020) Noshad et al. [2020] Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Noshad, M., Jankovic, I., Chen, J.H.: Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles. arXiv preprint arXiv:2007.12161 (2020) Li et al. [2020a] Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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[2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. 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Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. 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Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. 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Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. 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[2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. 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In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020)
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In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Graph neural network-based diagnosis prediction. Big Data 8(5), 379–390 (2020) Li et al. [2020b] Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. [2020b] Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Li, Y., Qian, B., Zhang, X., Liu, H.: Knowledge guided diagnosis prediction via graph spatial-temporal network. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 19–27 (2020). SIAM Liu et al. [2020a] Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205 (2020). IEEE Liu et al. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. 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[2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. 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In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. 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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., Zhou, Y.: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. International Journal of Machine Learning and Cybernetics 11(12), 2849–2856 (2020) Choi et al. [2017] Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. 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[2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017) Yue et al. [2020] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. 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In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S.M., Zhang, W., Zhang, P., Sun, H.: Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4), 1241–1251 (2020) Fritz et al. [2022] Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Fritz, C., Dorigatti, E., Rügamer, D.: Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany. Scientific Reports 12(1), 1–18 (2022) Hsieh et al. [2021] Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. 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KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. 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KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. 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Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. 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In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. 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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S., Jiang, X., Tang, J., Kim, Y.: Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence. Scientific reports 11(1), 1–13 (2021) Neave and Amaral [2011] Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. 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[2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020)
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[2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020)
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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Neave, G., Amaral, A.: Higher Education in Portugal 1974-2009: A Nation, a Generation. Springer, ??? (2011) An et al. [2018] An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) An, C., O’Malley., J.A., Rockmore, D.N., Stock, C.D.: Analysis of the U.S. Patient Referral Network. Statistics in medicine 37(5), 847–866 (2018) https://doi.org/10.1002/cncr.27633.Percutaneous Landon et al. [2018] Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Landon, B.E., Keating, N.L., Onnela, J.P., Zaslavsky, A.M., Christakis, N.A., James O’Malley, A.: Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Internal Medicine 178(1), 66–73 (2018) https://doi.org/10.1001/jamainternmed.2017.5034 Grover and Leskovec [2016] Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Grover, A., Leskovec, J.: Node2vec: Scalable Feature Learning for Networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 855–864 (2016) Data61 [2018] Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. 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Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. 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[2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Data61, C.: StellarGraph Machine Learning Library. GitHub (2018) Zhang et al. [2019] Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. 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Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020)
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Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Mining and Knowledge Discovery 33(6), 1953–1980 (2019) A. Grover [2016] A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) A. Grover, J.L.: Node2vec: Scalable feature learning for networks. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2016) McInnes et al. [2018] McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) McInnes, L., Healy, J., Saul, N., Großberger, L.: Umap: Uniform manifold approximation and projection. Journal of Open Source Software 3(29) (2018) Van der Maaten and Hinton [2008] Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) Lundberg and Lee [2017] Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(11) (2008) Tenenbaum et al. [2000] Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. 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- Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774 (2017) Shapley [1952] Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020)
- Shapley, L.S.: A Value for N-Person Games. RAND Corporation, Santa Monica, CA (1952). https://doi.org/10.7249/P0295 Slack et al. [2020] Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020) Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020)
- Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 180–186 (2020)
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