A Machine Learning-Enhanced Chat Application for the Identification of Mental Disorders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The prevalence of mental disorders is increasing, but they continue to be underdiagnosed and under addressed. Social media platforms offer novel opportunities for detecting potential mental health issues through the analysis of user-generated content. This paper presents a chat-based program developed using machine learning models trained on a dataset of comments from Reddit users. The program is capable of predicting the type of mental illness based on user input. This study provides a detailed comparison of various classification algorithms, including Naïve Bayes, Logistic Regression (LR), Support Vector Machines (SVM), and Random Forests (RF). Additionally, the paper discusses relevant machine learning techniques from previous studies. The results indicate that LR model, particularly with a uni-gram feature representation, outperforms other models with an accuracy of 0.81 and demonstrates the fastest processing speed. Future research directions include the integration of Large Language Models and the development of a multilingual chat interface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it