Machine learning applications related to suicide in military and Veterans: A scoping literature review.

OBJECTIVE

Suicide remains one of the main preventable causes of death among service members and veterans. Early detection and accurate prediction are essential components of effective suicide prevention strategies. Machine learning techniques have been explored in recent years with a specific focus on the assessment and prediction of multiple suicide-related outcomes, showing promising advancements. 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.

METHODS

A keyword search using PubMed, IEEE, ACM, and Google Scholar was conducted, and the PRISMA protocol was adopted for relevant study selection. Peer-reviewed original research in English targeting the assessment or prediction of suicide-related outcomes among service members and veteran populations was included. 1,110 studies were retrieved, and 32 satisfied the inclusion criteria and were included.

RESULTS

Thirty-two articles met the inclusion criteria. Despite these studies exhibiting significant variability in sample characteristics, data modalities, specific suicide-related outcomes, and the machine learning technologies employed, they 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 and have verified, on a large scale, risk factors previously detected by more manual analytic methods. 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.

CONCLUSION

In sum, machine learning analyses have identified risk factors associated with suicide in military populations, which span a wide range of psychological, biological, and sociocultural factors, highlighting the complexities involved in assessing suicide risk among service members and veterans. Some differences were noted between males and females. The diversity of these factors also demonstrates that effective prevention strategies must be comprehensive and flexible.