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Explainable Multitask Burnout Prediction Using Adaptive Deep Learning (EMBRACE) for Resident Physicians: Algorithm Development and Validation Study
Medical residency is characterized by high stress, long working hours, and demanding schedules, leading to widespread burnout among resident physicians. Although wearable sensors and machine learning (ML) models hold promise for predicting burnout, their lack of clinical explainability often limits their utility in health care settings.
Medigy Insights
A wearable-based deep learning system (EMBRACE) can accurately predict future physician burnout and explain the key contributing factors—like heart rate variability and prolonged sedentary periods—using explainable AI techniques to enhance clinical trust.
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Explainable AI–Driven Comparative Analysis of Machine Learning Models for Predicting HIV Viral Nonsuppression in Ugandan Patients: Retrospective Cross-Sectional Study
HIV viral suppression is essential for improving health outcomes and reducing transmission rates among people living with HIV. In Uganda, where HIV/AIDS is a major public health concern, machine …
