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Explainable Multitask Burnout Prediction Using Adaptive Deep Learning (EMBRACE) for Resident Physicians: Algorithm

Saima Alam¹, Mohammad Arif Ul Alam^2,3,4

¹Merrimack Health Methuen Hospital, Methuen, MA, United States.

|January 8, 2026

Summary

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This summary is machine-generated.

This study introduces EMBRACE, a novel AI framework using wearable sensors to predict and explain burnout in resident physicians. It offers actionable insights for early intervention and improved physician well-being.

Area of Science:

Machine Learning in Healthcare
Wearable Sensor Technology
Physician Burnout Research

Background:

Medical residents face high stress and burnout due to demanding schedules.
Existing machine learning models for burnout lack clinical explainability.
Wearable sensors offer potential for objective burnout prediction.

Purpose of the Study:

To present EMBRACE, an Explainable Multitask Burnout Prediction framework.
To predict and explain future burnout in resident physicians using adaptive deep learning.
To enhance clinical trust through explainable AI (XAI) techniques.

Main Methods:

Developed an adaptive multitask deep learning framework (EMBRACE).
Utilized wearable sensor data for predicting activities and burnout levels.

Keywords:

clinical explainability future burnout prediction health care informatics machine learning multitask learning wearable sensors

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Integrated SHAP (Shapley Additive Explanations) for model interpretability.

Validated on three datasets, including a proprietary resident physician dataset.

Main Results:

EMBRACE achieved high accuracy in predicting activities, burnout levels, and survey responses across datasets.
SHAP analysis identified key burnout predictors like heart rate variability and sedentary behavior.
91% of participants found the feature importance summaries satisfactory.

Conclusions:

EMBRACE offers a clinically explainable and actionable solution for early burnout detection.
The framework demonstrates robustness and generalizability across diverse datasets.
Future work includes scaling the model and assessing long-term impact on physician well-being.