Machine Learning Applications Related to Suicide in Military and Veterans: A Scoping Literature Review
Journal:
arXiv
Published Date:
May 18, 2025
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.
