IDEAS home Printed from https://ideas.repec.org/a/inm/orijoc/v36y2024i2p495-509.html
   My bibliography  Save this article

Bayesian Network Models for PTSD Screening in Veterans

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yi Tan & Prakash P. Shenoy & Ben Sherwood & Catherine Shenoy & Melinda Gaddy & Mary E. Oehlert, 2024. "Bayesian Network Models for PTSD Screening in Veterans," INFORMS Journal on Computing, INFORMS, vol. 36(2), pages 495-509, March.
  • Handle: RePEc:inm:orijoc:v:36:y:2024:i:2:p:495-509
    DOI: 10.1287/ijoc.2021.0174
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/ijoc.2021.0174
    Download Restriction: no

    File URL: https://libkey.io/10.1287/ijoc.2021.0174?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orijoc:v:36:y:2024:i:2:p:495-509. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.