@article{ref1,
title="A predictive analytic approach to planning combat stress control operations",
journal="International journal of stress management",
year="2019",
author="Campbell, Justin S. and Wallace, Meredith L. and Germain, Anne and Koffman, Robert L.",
volume="26",
number="2",
pages="120-131",
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 &quot;big&quot; behavioral health data sets. (PsycINFO Database Record (c) 2019 APA, all rights reserved)<p /> <p>Language: en</p>",
language="en",
issn="1072-5245",
doi="10.1037/str0000092",
url="http://dx.doi.org/10.1037/str0000092"
}