Machine Learning Applications Related to Suicide in Military and Veterans: A Scoping Literature Review
Yuhan Zhang, Yishu Wei, Yanshan Wang, Yunyu Xiao, COL, Ronald K. Poropatich, Gretchen L. Haas, Yiye Zhang, Chunhua Weng, Jinze Liu, Lisa A. Brenner, James M. Bjork, Yifan Peng
TL;DR
This scoping review synthesizes 32 studies (2014–2024) on using machine learning to predict suicide-related outcomes in active-duty military and veterans. It shows that ML can achieve moderate-to-high predictive accuracy (typical AUC 0.80–0.85) across diverse data modalities, including surveys, electronic health records, Army STARRS data, and textual or social media sources, with SI, SA, and SD as primary outcomes. The review identifies a broad set of risk factors—demographic, psychological, health-related, and military-experience factors—while highlighting gaps in data diversity, longitudinal modeling, and connection to clinical rationale. It emphasizes methodological and ethical considerations, urging richer data sources, advanced modeling (e.g., transformers, survival analysis), subgroup-focused analyses, and transparent reporting to translate ML insights into effective prevention strategies. The work underlines the potential of ML to support proactive, data-driven suicide prevention in military populations while calling for rigorous validation and cross-cultural generalizability to maximize impact.
Abstract
Suicide remains one of the main preventable causes of death among active service members and veterans. Early detection and prediction are crucial in suicide prevention. Machine learning techniques have yielded promising results in this area recently. This study aims to assess and summarize current research and provides a comprehensive review regarding the application of machine learning techniques in assessing and predicting suicidal ideation, attempts, and mortality among members of military and veteran populations. A keyword search using PubMed, IEEE, ACM, and Google Scholar was conducted, and the PRISMA protocol was adopted for relevant study selection. Thirty-two articles met the inclusion criteria. These studies consistently identified risk factors relevant to mental health issues such as depression, post-traumatic stress disorder (PTSD), suicidal ideation, prior attempts, physical health problems, and demographic characteristics. Machine learning models applied in this area have demonstrated reasonable predictive accuracy. However, additional research gaps still exist. First, many studies have overlooked metrics that distinguish between false positives and negatives, such as positive predictive value and negative predictive value, which are crucial in the context of suicide prevention policies. Second, more dedicated approaches to handling survival and longitudinal data should be explored. Lastly, most studies focused on machine learning methods, with limited discussion of their connection to clinical rationales. In summary, machine learning analyses have identified a wide range of risk factors associated with suicide in military populations. The diversity and complexity of these factors also demonstrates that effective prevention strategies must be comprehensive and flexible.