A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support
Bibliographic record
Abstract
Background: Decisions surrounding involuntary psychiatric treatment orders often involve complex clinical, legal, and ethical considerations, especially when patients lack decisional capacity and refuse treatment. In Quebec, these orders are issued by the Superior Court based on a combination of medical, legal, and behavioral evidence. However, no transparent, evidence-informed predictive tools currently exist to estimate the likelihood of full treatment order acceptance. This study aims to develop and evaluate a hybrid fuzzy logic-machine learning model to predict such outcomes and identify important influencing factors. Methods: A retrospective dataset of 176 Superior Court judgments rendered in Quebec in 2024 was curated from SOQUIJ, encompassing demographic, clinical, and legal variables. A Mamdani-type fuzzy inference system was constructed to simulate expert decision logic and output a continuous likelihood score. This score, along with structured features, was used to train a Random Forest classifier. Model performance was evaluated using accuracy, precision, recall and F1 score. A 10-fold stratified cross-validation was employed for internal validation. Feature importance was also computed to assess the influence of each variable on the prediction outcome. Results: The hybrid model achieved an accuracy of 98.1%, precision of 93.3%, recall of 100%, and a F1 score of 96.6. The most influential predictors were the duration of time granted by the court, duration requested by the clinical team, and age of the defendant. Fuzzy logic features such as severity, compliance, and a composite Burden_Score also significantly contributed to prediction accuracy. Only one misclassified case was observed in the test set, and the system provided interpretable decision logic consistent with expert reasoning. Conclusion: This exploratory study offers a novel approach for decision support in forensic psychiatric contexts. Future work should aim to validate the model across other jurisdictions, incorporate more advanced natural language processing for semantic feature extraction, and explore dynamic rule optimization techniques. These enhancements would further improve generalizability, fairness, and practical utility in real-world clinical and legal settings.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".