A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes

Abstract: High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential to gaining value from electronic health records (EHR) in the support of precision medicine. Despite technological advances, high-throughput phenotyping remains a challenge. This study compares three computational approaches to high-throughput phenotyping: a LLM incorporating generative AI, a NLP approach utilizing deep learning for span categorization, and a hybrid approach combining word vectors with machine learning. The approach that implemented GPT-4 (a LLM) demonstrated superior performance, suggesting that LLMs are poised to be the preferred method for high-throughput phenotyping of physician notes.