EMBRACE: Aprendizaje Profundo Adaptativo para la Predicción Multitarea Explicable del Agotamiento en Médicos...

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.

This paper presents EMBRACE (Explainable Multitask Burnout Prediction Using Adaptive Deep Learning), a novel framework designed to predict and explain future burnout in resident physicians through an adaptive multitask deep learning approach. The framework aims to provide clinically actionable and trustworthy burnout predictions by integrating explainable ML techniques.

EMBRACE applies deep multitask learning (3 tasks) using wearable sensor data for context-aware burnout prediction and explanation. The adaptive multitask learning framework predicts workplace activities and future burnout levels, and automatically completes a clinically validated burnout survey. Additionally, an explainability study was conducted using SHAP (Shapley Additive Explanations) to provide feature importance scores and visualizations for clinicians, enhancing the transparency and interpretability of the predictions. We evaluated the model on three datasets: (1) a collected dataset of 28 resident physicians (mean age 27.5, SD 3.5 years), over 2-7 days (average 3.6 days) with research protocols approved by the institutional review board (#2021-017) of Berkshire Medical Center, University of Massachusetts Chan Medical School; (2) the publicly available WESAD (Wearable Stress and Affect Detection) dataset from 15 participants; and (3) the SWELL-KW (SWELL Knowledge Work) dataset containing workplace stress and activity data from 25 participants (8 females and 17 males).

On our collected dataset, EMBRACE achieved 93% recall, 91% precision, and 0.91 R² error in predicting 5-class activities, 4-class future burnout levels, and 1 clinically explainable survey (Mini-Z with 10 questions). On the WESAD dataset, the model achieved 94.1% recall and 94.6% precision for 3-class stress level prediction. On the SWELL-KW dataset, EMBRACE obtained 89% recall, 86% precision, and 0.88 R² error in predicting 5-class activities, 3 burnout measures (joyful, satisfaction, and stress) with 2 classes on each measure, and 4 survey assessments (a total of 20 questions). The explainability study, using SHAP values, highlighted key contributing factors such as heart rate variability, sedentary activity duration, and interruptions, improving clinical trust and interpretation of burnout predictions. Of 23 participants, 21 (91%) reported satisfaction with the explainability of feature importance summaries.

EMBRACE provides a clinically explainable and actionable solution for early burnout detection in resident physicians, leveraging advanced ML techniques and SHAP-based explanations. Validation of proprietary and publicly available datasets demonstrates their robustness and generalizability. Future research may explore scaling the model across different clinical environments and assessing its long-term impact on health care outcomes and physician well-being.