Applying machine learning to explore the association between biological stress and near misses in emergency medicine residents.

ABSTRACT: Physician stress is associated with near misses and adverse medical events. However, little is known about physiological mechanisms linking stress to such events. We explored the utility of machine learning to determine whether the catabolic stress hormone cortisol and the anabolic, anti-stress hormone dehydroepiandrosterone sulfate (DHEA-S), as well as the cortisol to DHEA-S ratio relate to near misses in emergency medicine residents during active duty in a trauma 1 emergency department. Compared to statistical models better suited for inference, machine learning models allow for prediction in situations that have not yet occurred, and thus better suited for clinical applications. This exploratory study used multiple machine learning models to determine possible relationships between biom

SUBMITTER: Xu S 

PROVIDER: S-EPMC8903283 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature