Forecasting Hospital Mental-Health Admissions with a Novel Hybrid Deep Learning Architecture
Bibliographic record
Abstract
Forecasting hospital admissions, especially for mental health patients, is crucial for efficient resource allocation planning. This study used multivariate time-series data to forecast hospital admissions for mental health patients at the Thunder Bay Regional Health Sciences Centre in Ontario, Canada. The main contribution of this work is proposing a novel deep learning architecture, the Stacked-dilated-causal Convolutional Neural Network and Bidirectional Long Short-Term Memory (SCNN-BiLSTM), which outperformed other statistical and machine learning models. The hybrid architecture provides full history-coverage of the input window and maintains the causal structure of the input time series, making it suitable for long-term forecasting. For univariate one-week forecasting, among the commonly used methods, CNN-BiLSTM achieves the minimum Mean Absolute Percentage Error (MAPE) score (13.8). However, for 4-week forecasting, the MAPE score increases to 14.78. Modelling multivariate data reduces this score to 13.33. The multivariate model trained with the proposed architecture performs the best by achieving a MAPE score of 12.96 in 4-weeks forecasts. Training multivariate models using the SCNN-BiLSTM architecture developed in this study may be potentially utilized in other health forecasting domains to achieve improved forecasting accuracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".