This is a significant step towards creating evidence-based, transparent decision-support tools for clinicians and legal professionals. A must-read for anyone interested in #AI, #ForensicPsychiatry, or #MentalHealthLaw. Congrats again to Dr @AlexandreHudon !

What’s really impressive is the model's accuracy (98.1%) and its interpretability. It can pinpoint which factors—like the patient's age or the severity of their diagnosis—most influence the outcome. This transparency is key for ethical AI in sensitive fields.

The model uses a hybrid approach, combining fuzzy logic (to mimic expert reasoning) with a Random Forest classifier. It analyzes real-world court data from Quebec to predict whether a treatment order will be fully or partially accepted.

Excited to share this new paper by my colleague, Dr. @AlexandreHudon , published in @FrontiersInAI! He's developed a hybrid AI model to predict outcomes for psychiatric treatment orders—a crucial issue at the intersection of healthcare and law. ⚖️🧠🧵 https://t.co/ODN56hWVWt

Calling all mental health professionals! What questionnaires would you include in your database to track depressed patients' treatment responses? 🤔 Dream big—what tools would help you identify future predictors and achieve best practices?