Mental-Health Crisis Prediction in U.S. Veterans: Opportunities and Pitfalls of Machine-Learning on VA–DoD Data

Abstract

One of the most urgent public health issues facing American veterans is mental health issues and suicide. Leveraging large-scale Department of Veterans Affairs (VA) and Department of Defense (DoD) data, machine learning (ML) models offer a complementary approach to traditional screening by mining high-dimensional electronic health records, administrative registers, and clinical text. This narrative review synthesizes developments from 2015 to 2025 in ML-based prediction of suicidal behavior and related crises among veterans. Key findings indicate moderate but clinically useful discrimination across studies; for example, operational deployment of VA risk modeling concentrated risk such that the top 1% of risk scores contained roughly 10.7% of subsequent suicides, enabling targeted outreach. ML approaches can improve identification of at-risk veterans and strengthen preventive workflows, yet translation is limited by false positives, algorithmic bias, data integration challenges, and uncertain impact on mortality. The review discusses veteran-specific risk factors, data infrastructure, modeling paradigms, validation evidence, and ethical governance, and concludes with recommendations to prioritize prospective evaluation, equity audits, and integration strategies that couple prediction with effective intervention.