MentalSeek-Dx: Multimodal Mental Health Detection

• MentalSeek-Dx is a multimodal deep learning system that detects depression and PTSD early by analyzing clinical audio and textual data.
• It employs a two-branch neural architecture with BiLSTM for text and LSTM for audio, fusing features through element-wise averaging to calculate disorder risk scores.
• The system demonstrates robust performance with 92-93% accuracy on the DAIC-WOZ dataset and integrates seamlessly into clinical workflows via API-enabled EHR export.

MentalSeek-Dx is an advanced, multimodal deep learning system designed for the early and accurate detection of mental health conditions, specifically depression and post-traumatic stress disorder (PTSD), via automated analysis of audio and textual data from clinical interviews. The framework implements a two-branch neural architecture that performs parallel feature extraction and fusion, providing quantitative disorder risk scores to inform timely clinical interventions. MentalSeek-Dx operationalizes best practices in multimodal signal processing, deep learning, and clinical workflow integration, and is evaluated on clinically validated corpora with rigorous performance benchmarks (Singh et al., 6 Feb 2025).

1. System Architecture and Workflow

The MentalSeek-Dx platform is structured around a two-branch deep learning backbone integrating textual and audio features. Each input sample undergoes parallel preprocessing: transcripts are tokenized and embedded using BERT-base, while audio (.wav, 16 kHz) is processed for prosodic, spectral, and pitch-related features. The feature vectors are input into modality-specific neural networks—BiLSTM for text and LSTM for audio—followed by dimensionality reduction through dense layers. The outputs are fused by element-wise averaging and passed to a final dense (sigmoid-activated) layer producing a probability score between 0 and 1. This risk score serves as the prediction for disorder presence.

Block‐diagram:

Pipeline Step	Text Modality	Audio Modality
Preprocessing	Lowercase, clean, WordPiece/BERT	Resample, pre-emphasis, frame/window
Feature Extraction	BERT-[CLS] embedding (1×768)	MFCC, Chroma, Mel spec, etc. (1×193)
Neural Branch	BiLSTM(64)→Dropout→Dense(32,R)	LSTM(64)→Dropout→Dense(32,R)
Fusion & Output	\multicolumn{2}{c}{Elementwise avg→Dense(1,sigmoid)}

Inference latency is approximately 2 seconds per sample using a single 8-core CPU and one mid-range GPU, functioning as a Dockerized microservice with API endpoints for integration into electronic health record (EHR) ecosystems (Singh et al., 6 Feb 2025).

2. Feature Extraction and Modality Engineering

Textual Modality: Preprocessing includes tokenization, lowercasing, punctuation removal, and optional stopword filtering. BERT-base embeddings are extracted for each transcript, typically sourcing the [CLS] token as a context vector. The architecture can incorporate additional syntactic features, but the published framework relies exclusively on BERT representations.

Audio Modality: The audio signal is standardized (16 kHz mono, pre-emphasized, framed into 25 ms windows with 10 ms hop, Hamming windowed), and features are computed using librosa:

• MFCCs (13), Δ-MFCC (13), Δ²-MFCC (13)
• Chroma (12) and Mel-spectrogram (128)
• Spectral contrast (7), Tonnetz (6), Pitch (1)

All features are concatenated into a fixed-length (1×193) vector and optionally z-score normalized (Singh et al., 6 Feb 2025).

3. Network Architecture, Training, and Loss Formulation

Text Branch: Processes (batch_size,1,768) input via BiLSTM (64 units/direction), Dropout(p=0.3), Dense(32,ReLU).

Audio Branch: Processes (batch_size,1,193) input via unidirectional LSTM (64 units), Dropout(p=0.3), Dense(32,ReLU).

Fusion & Decision: The 32-dimensional outputs are averaged and passed through a Dense(1,sigmoid) layer for binary prediction.

Loss: Model training employs binary cross-entropy loss with an L2 regularization penalty:

$$L(\theta) \;=\; -\frac{1}{N}\sum_{i=1}^{N} \Bigl[y_i\log \hat{y}_i + (1-y_i)\log(1-\hat{y}_i)\Bigr] + \lambda\|\theta\|_2^2\,$$

Optimizer: Adam (learning rate $10^{-3}$), batch size 8, trained for 8–10 epochs with early stopping on validation loss (Singh et al., 6 Feb 2025).

4. Performance Evaluation and Comparative Results

Dataset: DAIC-WOZ corpus, comprising ~189 audio-transcript interview pairs labeled using clinician-administered PHQ-8 for depression and PTSD checklists.

Outcomes:

• Depression classification: 92% accuracy, AUC ≈ 0.95
• PTSD classification: 93% accuracy, AUC ≈ 0.96

Confusion Matrix (Depression):

	Non-Dep	Dep
Non-Dep	88%	12%
Dep	10%	90%

Baseline Comparisons:

Modality	Accuracy (%)
Text-only	85
Audio-only	80
Multimodal	92–93

These results demonstrate robust improvements over unimodal baselines, confirming the benefit of multimodal integration (Singh et al., 6 Feb 2025).

5. Risk Scoring and Clinical Deployment

The output probability $\hat y$ is interpreted as a disorder risk score:

• $\hat y < 0.4$: Low risk (routine monitoring)
• $0.4 \le \hat y < 0.7$: Medium risk (clinician notification)
• $\hat y \ge 0.7$: High risk (urgent intervention recommended)

Thresholds are adjustable in collaboration with clinical stakeholders. The backend returns structured outputs such as:

{ "score": 0.82, "risk_level": "High", "recommendation": "Immediate referral" }

The clinician dashboard features a color-coded risk gauge, time series score plots, and supports EHR export via HL7/FHIR (Singh et al., 6 Feb 2025).

6. Limitations, Ethical Safeguards, and Future Directions

Limitations: The DAIC-WOZ dataset’s population is predominantly English-speaking and US-based, which may bias model generalizability. The system does not currently process facial video or physiological signals (e.g., heart rate, GSR), and relies exclusively on BERT for text encode, limiting robustness to out-of-domain language.

Ethics: All data collection requires informed consent; data are encrypted both at rest and in transit, with strict HIPAA/GDPR compliance. The tool is designed as an adjunct to—not a replacement for—clinician judgment (Singh et al., 6 Feb 2025).

Planned Extensions:

• Incorporation of facial gesture/video analytics via 3D CNNs
• Integration of wearable sensor streams for further behavioral signal depth
• Cross-lingual adaptation using mBERT or XLM-R
• Deployment for real-time streaming inference during telehealth sessions

These enhancements seek to address modality gaps and further align MentalSeek-Dx with diverse, real-world clinical settings (Singh et al., 6 Feb 2025).

7. Significance and Clinical Utility

MentalSeek-Dx exemplifies state-of-the-art performance in early psychiatric risk estimation using a multimodal, interpretable deep learning pipeline. Its implementation offers rapid, transparent, and reproducible risk assessments, supporting timely interventions and continuous monitoring. The open, modular architecture facilitates clinical integration and future expansion into additional modalities and diagnostic categories, advancing the automated support of mental health care in practice (Singh et al., 6 Feb 2025).