Bayesian Network Models for PTSD Screening in Veterans

Yi Tan (), Prakash P. Shenoy (), Ben Sherwood (), Catherine Shenoy (), Melinda Gaddy () and Mary E. Oehlert ()
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Yi Tan: College of Business, The University of Alabama in Huntsville, Huntsville, Alabama 35899
Prakash P. Shenoy: School of Business, The University of Kansas, Lawrence, Kansas 66045
Ben Sherwood: School of Business, The University of Kansas, Lawrence, Kansas 66045
Catherine Shenoy: School of Business, The University of Kansas, Lawrence, Kansas 66045
Melinda Gaddy: VA Eastern Kansas Healthcare System, Leavenworth, Kansas 66048
Mary E. Oehlert: VA Eastern Kansas Healthcare System, Leavenworth, Kansas 66048

INFORMS Journal on Computing, 2024, vol. 36, issue 2, 495-509

Abstract: The prediction of posttraumatic stress disorder (PTSD) has gained a lot of interest in clinical studies. Identifying patients with a high risk of PTSD can guide mental healthcare workers when making treatment decisions. The main goal of this paper is to propose several Bayesian network (BN) models to assess the probability that a veteran has PTSD when first visiting a U.S. Department of Veteran Affairs (VA) facility seeking medical care. The current practice is to use a five-question test called PC-PTSD-5. We aim to use the PC-PTSD-5 test, which is currently administered to most incoming new patients, and demographic information, military service history, and medical history. We construct a Bayes information criterion score-based BN, a group L 2 -regularized BN ( GL 2 -regularized BN), and a naïve Bayes BN to assess the probability that a patient has PTSD. The GL 2 -regularized BN is a new method for constructing a BN motivated by some of the challenges of analyzing this data set. A secondary goal is to identify which features are important in predicting PTSD. We discover that the following features help compute the probability of PTSD: PC-PTSD-5, service-connected flag, combat flag, agent orange flag, military sexual trauma flag, traumatic brain injury, and age.

Keywords: PTSD prediction; probabilistic classification; Bayesian network; regularization method; healthcare analytics (search for similar items in EconPapers)
Date: 2024
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