Maintaining EHR-based Predictive Clinical Decision Support in Mental Health against Temporal Shifts

Electronic health record (EHR)-based risk prediction offers opportunities for early intervention in mental health, but models may remain in use as patient populations, care practices, and documentation change. Such temporal shifts can reduce predictive performance, while updating models from recent data alone may be unstable when adverse outcomes are uncommon. We developed Adversarial Drift-Aware Predictive Transfer (ADAPT), a distributionally robust approach to improve the temporal durability of mental health risk prediction. ADAPT uses variation in models estimated from previous calendar per

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