Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization.

ABSTRACT: Posttraumatic stress disorder (PTSD) develops in a substantial minority of emergency room admits. Inexpensive and accurate person-level assessment of PTSD risk after trauma exposure is a critical precursor to large-scale deployment of early interventions that may reduce individual suffering and societal costs. Toward this aim, we applied ensemble machine learning to predict PTSD screening status three months after severe injury using cost-effective and minimally invasive data. Participants (N = 271) were recruited at a Level 1 Trauma Center where they provided variables routinely collected at the hospital, including pulse, injury severity, and demographics, as well as psychological variables, including self-reported current depression, psychiatric history, and social support. Participant z

SUBMITTER: Papini S 

PROVIDER: S-EPMC6777842 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature