Dialogue System for Early Mental Illness Detection: Toward a Digital Twin Solution
Bibliographic record
Abstract
Mental health disorder rates have increased in recent years. In this research, we aim to address the barriers of stigma, accessibility, and affordability in mental healthcare by designing and developing a dialogue system that analyses the mental status of individuals. Additionally, it gives them personalized feedback based on the severity of the mental health problem. We propose a framework based on the concept of a Digital Twin for mental health, which incorporates recent advancements in technology to assess and classify the severity of mental health problems. The chatbot framework was designed in collaboration with a clinical psychiatrist and utilizes pre-trained BERT models, fine-tuned on the E-DAIC dataset, for the detection of various severity levels. The results of this study demonstrate the potential for our method to accurately detect signs of mental health problems with 69% accuracy, and high acceptability and usability with a score of 84.75%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".