Early identification of posttraumatic stress following military deployment: Application of machine learning methods to a prospective study of Danish soldiers

摘要

Background: Pro deployment identification of soldiers at risk for long-term posttraumatic stress psychopathology after home coming is important to guide decisions about deployment. Early post deployment identification can direct early interventions to those in need and thereby prevents the development of chronic psychopathology. Both hold significant public health benefits given large numbers of deployed soldiers, but has so far not been achieved. Here, we aim to assess the potential for pre- and early post deployment prediction of resilience or posttraumatic stress development in soldiers by application of machine learning (ML) methods.