ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1606250
A Hybrid Fuzzy Logic-Random Forest Model to Predict Psychiatric Treatment Order Outcomes: An Interpretable Tool for Legal Decision Support
Provisionally accepted- 1Institut universitaire en santé mentale de Montréal, Montreal, Canada
- 2Université de Montréal, Montréal, Canada
- 3Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, Canada
- 4Institut national de psychiatrie légale Philippe-Pinel, Montreal, Canada
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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.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 crossvalidation was employed for internal validation. Feature importance was also computed to assess the influence of each variable on the prediction outcome. 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.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.
Keywords: Fuzzy Logic, random forest, psychiatric treatment orders, Legal decision support, Forensic Psychiatry, machine learning, Interpretability, Law
Received: 04 Apr 2025; Accepted: 29 May 2025.
Copyright: © 2025 Hudon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Alexandre Hudon, Institut universitaire en santé mentale de Montréal, Montreal, Canada
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