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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 L2-regularized BN (GL2-regularized BN), and a naïve Bayes BN to assess the probability that a patient has PTSD. The GL2-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. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0174) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0174). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Substance use disorders (SUDs) are of great concern for health care providers working with military veteran populations. Systematic evaluation of progress and outcomes within the Veterans Affairs (VA) is a critical component of care provided for the veteran population. The Brief Addiction Monitor (BAM) is a 17-item instrument used within VA to assess substance use and related constructs among veterans participating in SUD care. Initial evaluations, using a version containing continuous items, suggested that the items form three factors reflecting substance use, risk factors, and protective factors. Subsequent work, using the BAM version containing Likert-style items collected from a single VA Medical Center sample, did not support the proposed 3-factor solution. The current study used a nationwide sample of 4955 veterans to evaluate the factor structure of the BAM and its usefulness over time. Exploratory factor analyses conducted did not provide evidence of the originally proposed BAM factor structure but instead supported a 4-factor model (reflecting alcohol use, stress, risk, and stability) formed from 13 of the items. Further analyses conducted within a structural equation modeling framework showed that the four-factor model exhibited invariance across occasions of measurement, although internal consistency was found to be low for most subscales. Results provide caution against using BAM subscale scores to track treatment outcomes over time. [ABSTRACT FROM AUTHOR]