Hudon Alexandre
Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Montreal, QC, Canada.
Department of Psychiatry, Institut universitaire en santé mentale de Montréal, Montreal, QC, Canada.
Front Artif Intell. 2025 Jun 17;8:1606250. doi: 10.3389/frai.2025.1606250. eCollection 2025.
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 cross-validation 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.
围绕非自愿精神科治疗令的决策通常涉及复杂的临床、法律和伦理考量，尤其是当患者缺乏决策能力并拒绝治疗时。在魁北克，这些命令由高等法院根据医学、法律和行为证据综合发布。然而，目前尚无透明的、基于证据的预测工具来估计全面治疗令被接受的可能性。本研究旨在开发和评估一种混合模糊逻辑 - 机器学习模型，以预测此类结果并识别重要影响因素。
从SOQUIJ整理了2024年在魁北克高等法院作出的176项判决的回顾性数据集，涵盖人口统计学、临床和法律变量。构建了一个Mamdani型模糊推理系统，以模拟专家决策逻辑并输出连续的可能性得分。该得分与结构化特征一起用于训练随机森林分类器。使用准确率、精确率、召回率和F1分数评估模型性能。采用10折分层交叉验证进行内部验证。还计算了特征重要性，以评估每个变量对预测结果的影响。
混合模型的准确率达到98.1%，精确率为93.3%，召回率为100%，F1分数为96.6。最具影响力的预测因素是法院批准的时间长度、临床团队请求的时间长度以及被告的年龄。诸如严重程度、依从性和综合负担评分等模糊逻辑特征也对预测准确性有显著贡献。在测试集中仅观察到一个误分类案例，并且该系统提供了与专家推理一致的可解释决策逻辑。
这项探索性研究为法医精神病学背景下的决策支持提供了一种新方法。未来的工作应旨在跨其他司法管辖区验证该模型，纳入更先进的自然语言处理以进行语义特征提取，并探索动态规则优化技术。这些改进将进一步提高在实际临床和法律环境中的通用性、公平性和实用价值。
