A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support
BackgroundDecisions 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 medic...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
|
| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1606250/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850103658036854784 |
|---|---|
| author | Alexandre Hudon Alexandre Hudon Alexandre Hudon Alexandre Hudon |
| author_facet | Alexandre Hudon Alexandre Hudon Alexandre Hudon Alexandre Hudon |
| author_sort | Alexandre Hudon |
| collection | DOAJ |
| description | BackgroundDecisions 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.MethodsA 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.ResultsThe 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.ConclusionThis 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. |
| format | Article |
| id | doaj-art-6767e0695add4e2499bacfd0286ab187 |
| institution | DOAJ |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-6767e0695add4e2499bacfd0286ab1872025-08-20T02:39:29ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-06-01810.3389/frai.2025.16062501606250A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision supportAlexandre Hudon0Alexandre Hudon1Alexandre Hudon2Alexandre Hudon3Centre de recherche de l’Institut universitaire en santé mentale de Montréal, Montreal, QC, CanadaDepartment of Psychiatry, Institut universitaire en santé mentale de Montréal, Montreal, QC, CanadaDepartment of Psychiatry, Institut national de psychiatrie légale Philippe-Pinel, Montreal, QC, CanadaDepartment of Psychiatry and Addictology, Université de Montréal Faculty of Medicine, Montreal, QC, CanadaBackgroundDecisions 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.MethodsA 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.ResultsThe 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.ConclusionThis 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.https://www.frontiersin.org/articles/10.3389/frai.2025.1606250/fullfuzzy logicRandom Forestpsychiatric treatment orderslegal decision supportforensic psychiatrymachine learning |
| spellingShingle | Alexandre Hudon Alexandre Hudon Alexandre Hudon Alexandre Hudon A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support Frontiers in Artificial Intelligence fuzzy logic Random Forest psychiatric treatment orders legal decision support forensic psychiatry machine learning |
| title | A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support |
| title_full | A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support |
| title_fullStr | A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support |
| title_full_unstemmed | A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support |
| title_short | A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support |
| title_sort | hybrid fuzzy logic random forest model to predict psychiatric treatment order outcomes an interpretable tool for legal decision support |
| topic | fuzzy logic Random Forest psychiatric treatment orders legal decision support forensic psychiatry machine learning |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1606250/full |
| work_keys_str_mv | AT alexandrehudon ahybridfuzzylogicrandomforestmodeltopredictpsychiatrictreatmentorderoutcomesaninterpretabletoolforlegaldecisionsupport AT alexandrehudon ahybridfuzzylogicrandomforestmodeltopredictpsychiatrictreatmentorderoutcomesaninterpretabletoolforlegaldecisionsupport AT alexandrehudon ahybridfuzzylogicrandomforestmodeltopredictpsychiatrictreatmentorderoutcomesaninterpretabletoolforlegaldecisionsupport AT alexandrehudon ahybridfuzzylogicrandomforestmodeltopredictpsychiatrictreatmentorderoutcomesaninterpretabletoolforlegaldecisionsupport AT alexandrehudon hybridfuzzylogicrandomforestmodeltopredictpsychiatrictreatmentorderoutcomesaninterpretabletoolforlegaldecisionsupport AT alexandrehudon hybridfuzzylogicrandomforestmodeltopredictpsychiatrictreatmentorderoutcomesaninterpretabletoolforlegaldecisionsupport AT alexandrehudon hybridfuzzylogicrandomforestmodeltopredictpsychiatrictreatmentorderoutcomesaninterpretabletoolforlegaldecisionsupport AT alexandrehudon hybridfuzzylogicrandomforestmodeltopredictpsychiatrictreatmentorderoutcomesaninterpretabletoolforlegaldecisionsupport |