ArticlePsychological medicine2024
Detecting suicide risk among U.S. servicemembers and veterans: a deep learning approach using social media data.
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 5 papers.
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Who cites it
5 citing papers in PubMed.
- Using smartphone surveys to predict next-week suicide attempts.Journal of psychopathology and clinical science · 2026Article
- Large Language Models for Mental Health Prediction: Scoping Review of Bias and Clinical Utility Documentation in 2019-2024.JMIR AI · 2026Review
- Machine learning applications related to suicide in military and Veterans: A scoping literature review.Journal of biomedical informatics · 2025Article
- Opportunities for Artificial Intelligence in Operational Medicine: Lessons from the United States Military.Bioengineering (Basel, Switzerland) · 2025Review
- Leveraging Large Language Models to Estimate Clinically Relevant Psychological Constructs in Psychotherapy Transcripts.Computational psychiatry (Cambridge, Mass.) · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundMilitary Servicemembers and Veterans are at elevated risk for suicide, but rarely self-identify to their leaders or clinicians regarding their experience of suicidal thoughts. We developed an algorithm to identify posts containing suicide-related content on a military-specific social media platform.
methodsPublicly-shared social media posts (
resultsThe best performing model was a deep learning (RoBERTa) model that incorporated post text and metadata and detected the presence of suicidal posts with relatively high sensitivity (0.85), specificity (0.96), precision (0.64), F1 score (0.73), and an area under the precision-recall curve of 0.84. Compared to non-suicidal posts, suicidal posts were more likely to contain explicit mentions of suicide, descriptions of risk factors (e.g. depression, PTSD) and help-seeking, and first-person singular pronouns.
conclusionsOur results demonstrate the feasibility and potential promise of using social media posts to identify at-risk Servicemembers and Veterans. Future work will use this approach to deliver targeted interventions to social media users at risk for suicide.
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Registered trials
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