Researchers have developed two smartwatch-based frameworks for detecting psychotic relapse. The first framework forecasts cardiac dynamics, while the second uses a multi-task approach to fuse sleep, motion, and cardiac data. Both models employ Transformer encoders and estimate predictive uncertainty using an ensemble of MLPs to generate daily anomaly scores. A late-fusion strategy combining both frameworks achieved an 8% improvement over the previous best baseline on the e-Prevention Grand Challenge dataset. AI
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IMPACT Novel application of AI in healthcare for early detection of mental health relapse using wearable sensor data.
RANK_REASON Academic paper detailing a new methodology for anomaly detection using wearable data. [lever_c_demoted from research: ic=1 ai=1.0]