A Multimodal Graph-Based Machine Learning Approach for Depression Detection
Bibliographic record
Abstract
Depression is a common mental disorder that affects millions of people worldwide. Psychological assessments remain the most commonly used diagnostic tools. However, this reliance highlights the opportunity to explore alternative approaches based on the use of machine learning models. This study explores a multimodal graph-based machine learning approach that combines electroencephalography (EEG), voice signals, demographic information, and psychological test results to detect depression. Two sets of graphs were generated using different combinations of features. The graph2vec model was then employed to generate embeddings for each graph set. Seven machine learning algorithms were trained using the embeddings as feature vectors. The results demonstrate competitive performance compared to those reported in the literature, achieving F1-scores above 0.95 while relying on less complex methods. The methodology employed and the results obtained are promising, highlighting the potential of graph-based approaches for performing multimodal classification tasks. However, there are limitations mainly related to associated with computational resources that should be analyzed in greater detail.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".