ArticleScientific reports2024
High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The impact of alcohol use disorder and PTSD comorbidity on risk of suicide attempt.Social psychiatry and psychiatric epidemiology · 2026Article
- Elucidating psychosocial outcome profiles as a function of aggregate genetic risk for suicide attempt and alcohol use disorder.Translational psychiatry · 2026Article
- Increasing value in the Veterans Affairs Healthcare System (VA) with precision health: a continuing landmark collaboration with the Department of Energy.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Dynamically predicting renal failure after development of diabetes across biobanks.PLOS digital health · 2026Article
- Evaluating model generalizability for suicide attempt risk prediction: traditional machine vs deep learning.Npj mental health research · 2026Article
- Life events extraction from healthcare notes for veteran acute suicide risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Machine learning applications related to suicide in military and Veterans: A scoping literature review.Journal of biomedical informatics · 2025Article
- Evaluating the factors influencing accuracy, interpretability, and reproducibility in the use of machine learning classifiers in biology to enable standardization.Scientific reports · 2025Article
- Veterans at High Risk for Post-COVID-19 Suicide Attempts or Other Self-Directed Violence.JAMA network open · 2025Article
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Authors and funding
28 authors.
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Abstract
We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide attempt, and overdose diagnoses with varying choices of study design and prediction methodology. Each model used twenty cross-sectional and 190 longitudinal variables observed in eight time intervals covering 7.5 years prior to the time of prediction. Ensembles of seven base models were created and fine-tuned with ten variables expected to change with study design and outcome definition in order to predict suicide and combined outcome in a prospective cohort. The ensemble models achieved c-statistics of 0.73 on 2-year suicide risk and 0.83 on the combined outcome when predicting on a prospective cohort of [Formula: see text] 4.2 M veterans. The ensembles rely on nonlinear base models trained using a matched retrospective nested case-control (Rcc) study cohort and show good calibration across a diversity of subgroups, including risk strata, age, sex, race, and level of healthcare utilization. In addition, a linear Rcc base model provided a rich set of biological predictors, including indicators of suicide, substance use disorder, mental health diagnoses and treatments, hypoxia and vascular damage, and demographics.
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