Quantitative forecasting of PTSD from early trauma responses: a Machine Learning application.

ABSTRACT: There is broad interest in predicting the clinical course of mental disorders from early, multimodal clinical and biological information. Current computational models, however, constitute a significant barrier to realizing this goal. The early identification of trauma survivors at risk of post-traumatic stress disorder (PTSD) is plausible given the disorder's salient onset and the abundance of putative biological and clinical risk indicators. This work evaluates the ability of Machine Learning (ML) forecasting approaches to identify and integrate a panel of unique predictive characteristics and determine their accuracy in forecasting non-remitting PTSD from information collected within 10 days of a traumatic event. Data on event characteristics, emergency department observations, and early

SUBMITTER: Galatzer-Levy IR 

PROVIDER: S-EPMC4252741 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature