Machine Learning and Embedding Models for Multimodal Mental Health Diagnostics
Bibliographic record
Abstract
This paper introduces a novel framework for analyzing interview data in different modalities to enhance the accuracy of mental health diagnostics. Addressing the growing need for effective detection of psychological disorders like depression and PTSD, our approach integrates text, audio, and video modalities, leveraging their complementary nature to provide a compre-hensive understanding of both verbal and non-verbal cues. We explore various embedding techniques, combined with machine learning and deep learning models-including Convolutional Neural Networks, and Bidirectional Long Short-Term Memory networks-to extract meaningful features. Our experiments on the E-DAIC dataset demonstrate the effectiveness of different data formats in identifying patterns indicative of mental health conditions. Specifically, text modality yielded the highest balanced accuracy, achieving 92.4% for depression and 88.1 % for PTSD. Moreover, switching the classification layer from Multilayer Perceptron to Support Vector Machine improved the accuracy in multiple configurations. These results compare the performance of different modalities analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".