Screening for psychological distress in healthcare workers using machine learning : a proof of concept

Abstract

The purpose of this study was to train and test preliminary models using two machine learning algorithms to identify healthcare workers at risk of developing anxiety, depression, and post-traumatic stress disorder. The study included data from a prospective cohort study of 816 healthcare workers collected using a mobile application during the first two waves of COVID-19. Each week, the participants responded to 11 questions and completed three screening questionnaires (one for anxiety, one for depression, and one for post-traumatic stress disorder). Then, the research team selected two questions (out of the 11), which were used with biological sex to identify whether scores on each screening questionnaire would be positive or negative. The analyses involved a fivefold cross-validation to test the accuracy of models based on logistic regression and support vector machines using cross-sectional and cumulative measures. The findings indicated that the models derived from the two questions and biological sex accurately identified screening scores for anxiety, depression, and post-traumatic stress disorders in 70% to 80% of cases. However, the positive predictive value never exceeded 50%, underlining the importance of collecting more data to train better models. Our proof of concept demonstrates the feasibility of using machine learning to develop novel models to screen for psychological distress in at-risk healthcare workers. Developing models with fewer questions may reduce burdens of active monitoring in practical settings by decreasing the weekly assessment duration.