ArticlePsychological medicine2024
Characterizing Veteran suicide decedents that were not classified as high-suicide-risk.
Article in Psychological medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Comparative Evaluation of Pretrained Large Language Models for Suicide Risk Prediction from Clinical Notes in U.S. Veterans.medRxiv : the preprint server for health sciences · 2026Article
- Personalizing Suicide Risk Assessment: Machine Learning Extraction of Cross-Modal Interactions Between Psychosocial and Demographic Factors in Veterans.medRxiv : the preprint server for health sciences · 2026Article
- Evaluating Evidence-Based Psychotherapy Utilization Patterns Among Suicide-Risk-Stratified Veterans Diagnosed with Major Depressive Disorder.Administration and policy in mental health · 2026Article
- Enhancing personalized suicide risk prediction for VA patients by integrating discrete natural language processing models.Translational psychiatry · 2026Article
- Investigating the Differential Impact of Psychosocial Factors by Patient Characteristics and Demographics on Veteran Suicide Risk Through Machine Learning Extraction of Cross-Modal Interactions.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Using Natural Language Processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients.Journal of psychiatric research · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundAlthough the Department of Veterans Affairs (VA) has made important suicide prevention advances, efforts primarily target high-risk patients with documented suicide risk, such as suicidal ideation, prior suicide attempts, and recent psychiatric hospitalization. Approximately 90% of VA patients that go on to die by suicide do not meet these high-risk criteria and therefore do not receive targeted suicide prevention services. In this study, we used national VA data to focus on patients that were not classified as high-risk, but died by suicide.
methodsOur sample included all VA patients who died by suicide in 2017 or 2018. We determined whether patients were classified as high-risk using the VA's machine learning risk prediction algorithm. After excluding these patients, we used principal component analysis to identify moderate-risk and low-risk patients and investigated demographics, service-usage, diagnoses, and social determinants of health differences across high-, moderate-, and low-risk subgroups.
resultsHigh-risk (
conclusionsStudy expands epidemiological understanding about non-high-risk suicide decedents, historically understudied and underserved populations. Findings raise concerns about reliance on machine learning risk prediction models that may be biased by relative underrepresentation of racial/ethnic minorities within health system.
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