ArticleMolecular psychiatry2017
Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).
Article in Molecular psychiatry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 4 of them syntheses that pooled it.
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Who cites it
85 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis.Frontiers in digital health · 2022Pooled it
- Improving Suicide Prevention Through Evidence-Based Strategies: A Systematic Review.The American journal of psychiatry · 2021Pooled it
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- Risk Model-Guided Clinical Decision Support for Suicide Screening: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.The annals of applied statistics · 2026Article
- Evaluating model generalizability for suicide attempt risk prediction: traditional machine vs deep learning.Npj mental health research · 2026Article
- Advantages and disadvantages of artificial intelligence in the prediction and prevention of suicide.Postepy psychiatrii neurologii · 2026Review
- Development and External Validation of a Prediction Model to Identify Suicide Attempters in Treatment-Naive Adolescents With Major Depressive Disorder.Depression and anxiety · 2026Article
- Behavior of prediction performance metrics with rare events.Journal of clinical epidemiology · 2026Article
- Predicting suicide death among veterans after psychiatric hospitalization using transformer based models with social determinants and NLP.Scientific reports · 2025Article
- Machine learning applications related to suicide in military and Veterans: A scoping literature review.Journal of biomedical informatics · 2025Article
- Multimodal Machine Learning Prediction of 12-Month Suicide Attempts in Bipolar Disorder.Bipolar disorders · 2025Article
- Evaluating Machine Learning for Predicting Youth Suicidal Behavior Up to 1 Year After Contact With Mental-Health Specialty Care.Clinical psychological science : a journal of the Association for Psychological Science · 2025Article
- Article
- Automated detection and prediction of suicidal behavior from clinical notes using deep learning.PloS one · 2025Article
- Importance of variables from different time frames for predicting self-harm using health system data.Journal of biomedical informatics · 2024Article
- Acceptability of linking individual credit, financial, and public records data to healthcare records for suicide risk machine learning models.JAMIA open · 2024Article
- Importance of variables from different time frames for predicting self-harm using health system data.medRxiv : the preprint server for health sciences · 2024Article
- Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records.Molecular psychiatry · 2024Article
25 more citing papers are in PubMed but not listed here.
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Authors and funding
19 authors.
Funding
Abstract
The 2013 US Veterans Administration/Department of Defense Clinical Practice Guidelines (VA/DoD CPG) require comprehensive suicide risk assessments for VA/DoD patients with mental disorders but provide minimal guidance on how to carry out these assessments. Given that clinician-based assessments are not known to be strong predictors of suicide, we investigated whether a precision medicine model using administrative data after outpatient mental health specialty visits could be developed to predict suicides among outpatients. We focused on male nondeployed Regular US Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naive Bayes, random forests, support vector regression and elastic net penalized regression) were explored. Of the Army suicides in 2004-2009, 41.5% occurred among 12.0% of soldiers seen as outpatient by mental health specialists, with risk especially high within 26 weeks of visits. An elastic net classifier with 10-14 predictors optimized sensitivity (45.6% of suicide deaths occurring after the 15% of visits with highest predicted risk). Good model stability was found for a model using 2004-2007 data to predict 2008-2009 suicides, although stability decreased in a model using 2008-2009 data to predict 2010-2012 suicides. The 5% of visits with highest risk included only 0.1% of soldiers (1047.1 suicides/100 000 person-years in the 5 weeks after the visit). This is a high enough concentration of risk to have implications for targeting preventive interventions. An even better model might be developed in the future by including the enriched information on clinician-evaluated suicide risk mandated by the VA/DoD CPG to be recorded.
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