This paper is in the following e-collection/theme issue:

Foundations of AI (61) Research Methodology - AI (42) Physician and Healthcare Worker Burnout (152) Wearable Devices and Sensors (1163) Machine Learning (2121) Clinical Informatics (1348)

Published on in Vol 5 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57025, first published .
Explainable Multitask Burnout Prediction Using Adaptive Deep Learning (EMBRACE) for Resident Physicians: Algorithm Development and Validation Study

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

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

Authors of this article:

Saima Alam1 Author Orcid Image ;   Mohammad Arif Ul Alam2, 3, 4 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Saima Alam   1 , MD ;   Mohammad Arif Ul Alam   2, 3, 4 , PhD

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

2 Richard A. Miner School of Computer and Information Sciences, University of Massachusetts Lowell, Lowell, MA, United States

3 University of Massachusetts Chan Medical School, Worcester, MA, United States

4 National Institute on Aging, National Institute of Health, Bethesda, MD, United States

Corresponding Author:

  • Mohammad Arif Ul Alam, PhD
  • Richard A. Miner School of Computer and Information Sciences
  • University of Massachusetts Lowell
  • 1 University Ave
  • Lowell, MA 01854
  • United States
  • Phone: 1 978-934-1971
  • Email: mohammadariful_alam@uml.edu