Prediction of Suicidal Ideation in the Canadian Community <i>Health Survey - Mental Health Component</i> Using Deep Learning
Bibliographic record
Abstract
Abstract Introduction Suicidal ideation (SI) is prevalent in the general population, and is a prominent risk factor for suicide. However, predicting which patients are likely to have SI remains a challenge. Deep Learning (DL) may be a useful tool in this context, as it can be used to find patterns in complex, heterogeneous, and incomplete psychiatric datasets. An automated screening system for SI could help prompt clinicians to be more attentive to patients at risk for suicide. Methods Using the Canadian Community Health Survey - Mental Health Component, we trained a DL model based on 23,859 survey responses to predict lifetime SI on an individual patient basis. Models were created to predict both lifetime and last 12 month SI. We reduced 582 possible model parameters captured by the survey to 96 and 21 feature versions of the models. Models were trained using an undersampling procedure that balanced the training set between SI and non-SI respondents; validation was done on held-out data. Results AUC was used as the main model metric. For lifetime SI, the 96 feature model had an AUC of 0.79 and the 21 feature model had an AUC of 0.75. For SI in the last 12 months the 96 feature model had an AUC of 0.76 and the 21 feature model had an AUC of 0.69. DL outperformed random forest classifiers. Discussion Although requiring further study to ensure clinical relevance and sample generalizability, this study is a proof-of-concept for the use of DL to improve prediction of SI. This kind of model would help start conversations with patients which could lead to improved care and, it is hoped, a reduction in suicidal behavior.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".