ArticleJournal of affective disorders2021
Predicting suicide attempts and suicide deaths among adolescents following outpatient visits.
Article in Journal of affective disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning algorithms and their predictive accuracy for suicide and self-harm: Systematic review and meta-analysis.PLoS medicine · 2025Pooled it
- Pooled it
- Article
- Validation of a self-harm prediction model in Black youth seeking outpatient mental health care.American journal of epidemiology · 2026Article
- Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.The annals of applied statistics · 2026Article
- Predicting suicide attempts in a high-risk clinical cohort of adolescents using machine-learning.European child & adolescent psychiatry · 2026Article
- Evaluating model generalizability for suicide attempt risk prediction: traditional machine vs deep learning.Npj mental health research · 2026Article
- Development and validation of a machine learning model to identify individuals at high risk for psychotic disorders using medical record data.BMC psychiatry · 2026Article
- Large language models in adolescent suicide prevention: from language signals to accountable action.Frontiers in public health · 2026Review
- Behavior of prediction performance metrics with rare events.Journal of clinical epidemiology · 2026Article
- Exploring the Perspectives of Pediatric Health Care Providers, Youth Patients, and Caregivers on Machine Learning Suicide Risk Classification: Mixed Methods Study.Journal of medical Internet research · 2025Article
- Review
- Use of Artificial Intelligence in Adolescents' Mental Health Care: Systematic Scoping Review of Current Applications and Future Directions.JMIR mental health · 2025Article
- Developing a suicide risk model for use in the Indian Health Service.Npj mental health research · 2024Article
- Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records.Molecular psychiatry · 2024Article
- High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning.Scientific reports · 2024Article
- Determining Distinct Suicide Attempts From Recurrent Electronic Health Record Codes: Classification Study.JMIR formative research · 2024Article
- Implementation of Suicide Risk Estimation Analytics to Support Mental Health Care for Quality Improvement.JAMA network open · 2022Article
- What Makes a Useful "Predictor" of Risk for Suicide Attempt?JAMA psychiatry · 2022Article
- Adolescent, Parent, and Provider Perceptions of a Predictive Algorithm to Identify Adolescent Suicide Risk in Primary Care.Academic pediatricsArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundFew studies report on machine learning models for suicide risk prediction in adolescents and their utility in identifying those in need of further evaluation. This study examined whether a model trained and validated using data from all age groups works as well for adolescents or whether it could be improved.
methodsWe used healthcare data for 1.4 million specialty mental health and primary care outpatient visits among 256,823 adolescents across 7 health systems. The prediction target was 90-day risk of suicide attempt following a visit. We used logistic regression with least absolute shrinkage and selection operator (LASSO) and generalized estimating equations (GEE) to predict risk. We compared performance of three models: an existing model, a recalibrated version of that model, and a newly-learned model. Models were compared using area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value.
resultsThe AUC produced by the existing model for specialty mental health visits estimated in adolescents alone (0.796; [0.789, 0.802]) was not significantly different than the AUC of the recalibrated existing model (0.794; [0.787, 0.80]) or the newly-learned model (0.795; [0.789, 0.801]). Predicted risk following primary care visits was also similar: existing (0.855; [0.844, 0.866]), recalibrated (0.85 [0.839, 0.862]), newly-learned (0.842, [0.829, 0.854]). LIMITATIONS: The models did not incorporate non-healthcare risk factors. The models relied on ICD9-CM codes for diagnoses and outcome measurement.
conclusionsPrediction models already in operational use by health systems can be reliably employed for identifying adolescents in need of further evaluation.
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