LEVERAGING MACHINE LEARNING TO IMPROVE TRAUMA-RELATED HEALTH OUTCOMES IN MILITARY MEMBERS, VETERANS, PUBLIC SAFETY PERSONNEL, AND CIVILIANS

Summary: Post-traumatic stress disorder (PTSD) can severely impact the mental health of military members, Veterans, public safety personnel (PSP), and civilians, including how they feel and how they manage daily living. Homewood Health Centre and the University of Western Ontario amassed a large secondary clinical dataset of treatment-seeking military, Veteran, PSP, and civilian samples. Using machine learning models, we will use a data-driven approach to uncover how PTSD symptom severity, dissociation, childhood trauma, functional impairment, emotion dysregulation, and other relevant symptoms predict the severity of PTSD symptoms, as well as predict improvement of PTSD symptoms after treatment. We expect that our results will support the use of machine learning models in predicting trauma-related clinical outcomes, as well as inform treatment guidelines, and guide a personalized medicine approach by tailoring treatments based on individual-level characteristics.

Funding: CIMVHR, MITACS, NSERC

Project Lead: Anna Park

Team: Margaret C. McKinnon (Co-PI), Ruth A. Lanius (PI), Patrick Martin (Co-PI), Andrew A. Nicholson (Co-PI), Anna H. Park, Herry Patel, James Mirabelli, Stephanie J. Eder, David Steyrl, Charlene O'Connor