Probabilistic graphical models for predicting post-traumatic stress disorder in US veterans

What is it about?

The prediction of post-traumatic stress disorder (PTSD) has gained a lot of interest in clinical studies. Identifying veterans 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 networks (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.

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Why is it important?

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.