Citation

Campbell JS, Wallace ML, Germain A, Koffman RL. Int. J. Stress Manag. 2019; 26(2): 120-131.

Copyright

(Copyright © 2019, American Psychological Association)

DOI

10.1037/str0000092

PMID

unavailable

Abstract

Combat and operational stress control (COSC) surveys guide allocation of high-demand, low-quantity mental health assets to support combat-deployed U.S. forces. The current article describes an innovative application of machine learning, decision tree analysis, to predict unit-level risk for combat mental health outcomes like posttraumatic stress disorder (PTSD). The initial algorithm was developed from large population-based COSC surveys conducted in 2007/2008 in Iraq and Afghanistan. The algorithm was validated in a separate sample of COSC surveys collected in Afghanistan in 2010. Using the applied field standard for high-risk units (i.e., 10% or more of the unit screening at risk for PTSD), the decision tree algorithm correctly identified 100% of units considered high risk for PTSD in the validation sample, while only misclassifying 10% (3 of 31 units) in the independent 2010 sample. This article provides a template by which future efforts to enhance COSC can be aided by iterative approaches to analyzing "big" behavioral health data sets. (PsycINFO Database Record (c) 2019 APA, all rights reserved)

Language: en

Keywords

Algorithms; Combat Experience; Decision Making; Machine Learning; Mental Health; Posttraumatic Stress Disorder; Statistical Analysis; Stress