Enhancing Risk Prediction in Mental Health Using Ensemble Hybrid Models and Administrative Healthcare Data with Irregular Intervals
Bibliographic record
Abstract
Risk prediction estimates the probability of future adverse outcomes for high-risk individuals to enable early intervention. Among all machine learning models, deep learning models can enhance risk predictions with respect to mental health (MH), where administrative healthcare data (Admin-HD) contain complex, irregular temporal information that a single model may not fully capture. This study aims to develop an ensemble hybrid deep-learning model (EnH-DL) to improve performance and reduce errors on unseen data compared to available deep-learning models. The model was evaluated in a cohort of individuals diagnosed with addiction or mental illness (AMH) to predict the risk of outcomes, such as the first healthcare encounter indicating homelessness (FHE-H) and the first healthcare encounters involving police (FHE-P). A sliding window and matrix-based representation were used in the preprocessing phase. Gated recurrent units (GRUs) with time decay, a one-dimensional (1D) convolutional neural network (CNN), and an attention function were integrated to build the EnH-DL. The results showed an average improvement in the area under the curve (AUC) by 2.5% and sensitivity by over 1.5%. An error reduction of over 0.22 indicates improved model reliability for unseen data. Tested in a clinical demand window to simulate real-world settings, the model achieved an 83% AUC and 79% sensitivity for FHE-H on a highly imbalanced test set. In conclusion, EnH-DL outperformed existing models for multiple MH outcomes using Admin-HD.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".