EMBRACE: Explainable Multitask Burnout Prediction for Resident Physicians using Adaptive Deep Learning

Abstract

AbstractMedical residency is associated with long working hours, demanding schedules, and high stress levels, which can lead to burnout among resident physicians. Although wearable and machine learning-based interventions can be useful in predicting potential burnout, existing models fail to clinically explain their predictions, thereby undermining the trustworthiness of the research findings and rendering the intervention apparently useless to residents. This paper develops, EMBRACE,ExplainableMultitaskBurnout pRediction usingAdaptivEdeep learning, that employs a novel framework for predicting burnout that is clinically explainable. At first, we develop, a wearable sensor based improved workplace activity and stress detection algorithm, using deep multi-task learning. Next, we present a novel Adaptive Multi-Task Learning (MTL) framework built on top of our activity and stress detection algorithm, to automatically detect burnout. Additionally, this model also completes the resident burnout survey automatically such a way that it can clinically estimate the same burnout level i.e., clinically explainable and trustworthy estimation. We evaluated the efficacy and explainability of EMBRACE using a real-time data collected from 28 resident physicians (2-7 days each) with appropriate IRB approval (IRB# 2021-017).