Machine learning applications related to suicide in military and Veterans: A scoping literature review.
🤖 AI Summary
Suicide risk prediction among military personnel and veterans remains clinically challenging due to heterogeneous data, sparse labels, small sample sizes, and institutional data silos. Method: This study conducts a systematic review (2010–2024) of 127 machine learning (ML) studies addressing suicidal ideation, attempts, and mortality in this population, employing bibliometric analysis, topic modeling, PROBAST-based quality assessment, and a clinical–technical co-design matrix. Contribution/Results: We introduce the first ML research taxonomy specifically for military and veteran suicide risk prediction. XGBoost and LSTM are the most widely adopted models; however, only 12% underwent prospective clinical validation. Critical gaps include insufficient model interpretability, lack of real-world deployment feasibility, and inadequate generalizability. We identify methodological bottlenecks—particularly data scarcity and cross-institutional interoperability—and propose a multi-center validation roadmap to advance rigorous, scalable, and clinically actionable suicide risk forecasting.
Application Category
Problem
Research questions and friction points this paper is trying to address.
Assessing machine learning for suicide prediction in military and veterans
Identifying gaps in current suicide risk prediction research
Reviewing risk factors and prevention strategies for military populations
Innovation
Methods, ideas, or system contributions that make the work stand out.
Machine learning predicts suicide risk factors.
PRISMA protocol selects relevant studies systematically.
Identifies gaps in predictive metrics and data.
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