ArticleApplied clinical informatics2021
Evaluation of Electronic Health Record-Based Suicide Risk Prediction Models on Contemporary Data.
Article in Applied clinical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for self-harm and suicide: a systematic review and critical appraisal.BMC medicine · 2025Pooled it
- Artificial Intelligence in Psychiatry: Five Decades of Progress and Persistent Translational Challenges.Psychiatric research and clinical practice · 2026Article
- Validation of self‑harm prediction models among formerly incarcerated individuals using health data.Health & justice · 2026Article
- Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.The annals of applied statistics · 2026Article
- Clinicians' Visual Attention During Suicide Screening Encounters: An Exploratory Eye-Tracking Study.medRxiv : the preprint server for health sciences · 2026Article
- Behavior of prediction performance metrics with rare events.Journal of clinical epidemiology · 2026Article
- Screening and Risk Algorithms for Detecting Pediatric Suicide Risk in the Emergency Department.JAMA network open · 2025Article
- Suicidality Screening: Ensuring Providers Do Not Miss Adolescents at High Risk through Clinical Decision Support.Applied clinical informatics · 2025Article
- Enhancing Suicide Attempt Risk Prediction Models with Temporal Clinical Note Features.Applied clinical informatics · 2024Article
- Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records.Translational psychiatry · 2024Article
- Suicide Risk Screening for Head and Neck Cancer Patients: An Implementation Study.Applied clinical informatics · 2024Article
- Determining Distinct Suicide Attempts From Recurrent Electronic Health Record Codes: Classification Study.JMIR formative research · 2024Article
- Machine Learning Prediction of Suicide Risk Does Not Identify Patients Without Traditional Risk Factors.The Journal of clinical psychiatry · 2022Article
- Integration of Risk Scores and Integration Capability in Electronic Patient Records.Applied clinical informatics · 2022Article
- Design and Evaluation of a Postpartum Depression Ontology.Applied clinical informatics · 2022Article
- Real-world implementation and predictive performance of suicide prediction models: A systematic review and meta-analysis.Digital healthReview
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11 authors.
Funding
Abstract
backgroundSuicide risk prediction models have been developed by using information from patients' electronic health records (EHR), but the time elapsed between model development and health system implementation is often substantial. Temporal changes in health systems and EHR coding practices necessitate the evaluation of such models in more contemporary data.
objectivesA set of published suicide risk prediction models developed by using EHR data from 2009 to 2015 across seven health systems reported c-statistics of 0.85 for suicide attempt and 0.83 to 0.86 for suicide death. Our objective was to evaluate these models' performance with contemporary data (2014-2017) from these systems.
methodsWe evaluated performance using mental health visits (6,832,439 to mental health specialty providers and 3,987,078 to general medical providers) from 2014 to 2017 made by 1,799,765 patients aged 13+ across the health systems. No visits in our evaluation were used in the previous model development. Outcomes were suicide attempt (health system records) and suicide death (state death certificates) within 90 days following a visit. We assessed calibration and computed c-statistics with 95% confidence intervals (CI) and cut-point specific estimates of sensitivity, specificity, and positive/negative predictive value.
resultsModels were well calibrated; 46% of suicide attempts and 35% of suicide deaths in the mental health specialty sample were preceded by a visit (within 90 days) with a risk score in the top 5%. In the general medical sample, 53% of attempts and 35% of deaths were preceded by such a visit. Among these two samples, respectively, c-statistics were 0.862 (95% CI: 0.860-0.864) and 0.864 (95% CI: 0.860-0.869) for suicide attempt, and 0.806 (95% CI: 0.790-0.822) and 0.804 (95% CI: 0.782-0.829) for suicide death.
conclusionPerformance of the risk prediction models in this contemporary sample was similar to historical estimates for suicide attempt but modestly lower for suicide death. These published models can inform clinical practice and patient care today.
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