ArticleTranslational psychiatry2020
Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records.
Article in Translational psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 7 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 7 syntheses or guidelines pooled it, 106 citations in OpenAlex.
- Integrating Neuroimaging and Machine Learning to Predict Mental Disorder Outcomes: A Systematic Review.Advances in experimental medicine and biology · 2026Pooled it
- Prediction models for self-harm and suicide: a systematic review and critical appraisal.BMC medicine · 2025Pooled it
- Machine learning algorithms and their predictive accuracy for suicide and self-harm: Systematic review and meta-analysis.PLoS medicine · 2025Pooled it
- Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies.BMC medical informatics and decision making · 2024Pooled it
- The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review.Frontiers in 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
- Social determinants of health in electronic health records and their impact on analysis and risk prediction: A systematic review.Journal of the American Medical Informatics Association : JAMIA · 2020Pooled it
- Article
- Insights into middle-aged men (40-70) who died by suicide: a nationwide mixed methods study in the Netherlands.BMC public health · 2026Article
- Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.The annals of applied statistics · 2026Article
- Youth visiting the emergency department after a suicide attempt, suicidal ideation or non-suicidal self-injury: trends, repeat visits and costs.BJPsych open · 2026Article
- Life events extraction from healthcare notes for veteran acute suicide risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Validation of a Risk-Prediction Model in the Presence of Outcome Misclassification.Statistics in medicine · 2026Article
- A phenome-wide association and Mendelian randomization study for suicide attempt within UK Biobank.Molecular psychiatry · 2026Article
- Article
- Machine learning for early screening of influenza A-associated invasive pulmonary aspergillosis in hospitalized patients: a real-world study.Frontiers in cellular and infection microbiology · 2026Article
- Simultaneous forecasting of vital sign trajectories in the ICU.Scientific reports · 2025Article
- Perspectives on Using Artificial Intelligence to Derive Social Determinants of Health Data From Medical Records in Canada: Large Multijurisdictional Qualitative Study.Journal of medical Internet research · 2025Article
- Teaching Artificial Intelligence from Conceptual Foundations: A Roadmap for Psychiatry Training Programs.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2025Article
- pyDeid: an improved, fast, flexible, and generalizable rule-based approach for deidentification of free-text medical records.JAMIA open · 2025Article
Corrections and comments
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Authors and funding
16 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a population-based risk stratification surveillance system. Advanced machine-learning algorithms and deep neural networks were utilized to build models with the data from electronic health records (EHRs). A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following 1-year time period. Risk scores were subjected to individual-level analysis in order to aid in the interpretation of the results for health-care providers managing the at-risk cohorts. The 1-year suicide attempt risk model attained an area under the curve (AUC ROC) of 0.792 and 0.769 in the retrospective and prospective cohorts, respectively. The suicide attempt rate in the "very high risk" category was 60 times greater than the population baseline when tested in the prospective cohorts. Mental health disorders including depression, bipolar disorders and anxiety, along with substance abuse, impulse control disorders, clinical utilization indicators, and socioeconomic determinants were recognized as significant features associated with incident suicide attempt.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.