ArticleGeneral hospital psychiatry
Predicting suicide death after emergency department visits with mental health or self-harm diagnoses.
Article in General hospital psychiatry. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning algorithms and their predictive accuracy for suicide and self-harm: Systematic review and meta-analysis.PLoS medicine · 2025Pooled it
- Comparing Risk Prediction for Suicide Attempts and Deaths After Emergency Department Visits for Individuals With Mental Health Disorders.The Journal of clinical psychiatry · 2026Article
- Comparing patterns of recent mental health service use for predicting suicidal events following emergency department mental health visits in the United States: A national cohort study.Social psychiatry and psychiatric epidemiology · 2026Article
- Levels of matrix metalloproteinase 9 (MMP-9) are elevated in persons with major depressive disorder who have had a recent suicide attempt.Brain, behavior, & immunity - health · 2026Article
- Suicide Risk in 8- to 12-Year-Old Children: Retrospective Review of Emergency Department Electronic Health Records.JAACAP open · 2026Article
- The relationship between secondhand smoke exposure in Chinese children and adolescents and renal function and hyperuricemia: a cross-sectional study.Frontiers in pediatrics · 2026Article
- Estimation of chlorophyll content in cotton leaves using UAV RGB imagery.Frontiers in plant science · 2026Article
- A hierarchical machine learning model for predicting self-harm and suicidal behaviour in hospitalised patients with schizophrenia using clinical history and nursing observations.Frontiers in psychiatry · 2026Article
- A cohort study of predictors of short-term nonfatal suicidal and self-harm events among individuals with mental health disorders treated in the emergency department.Journal of psychiatric research · 2025Article
- Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.Turkish journal of emergency medicineReview
- Machine-learning identified suicide risk and emergency department inpatient admission.General hospital psychiatryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
objectiveUse health records data to predict suicide death following emergency department visits.
methodsElectronic health records and insurance claims from seven health systems were used to: identify emergency department visits with mental health or self-harm diagnoses by members aged 11 or older; extract approximately 2500 potential predictors including demographic, historical, and baseline clinical characteristics; and ascertain subsequent deaths by self-harm. Logistic regression with lasso and random forest models predicted self-harm death over 90 days after each visit.
resultsRecords identified 2,069,170 eligible visits, 899 followed by suicide death within 90 days. The best-fitting logistic regression with lasso model yielded an area under the receiver operating curve of 0.823 (95% CI 0.810-0.836). Visits above the 95th percentile of predicted risk included 34.8% (95% CI 31.1-38.7) of subsequent suicide deaths and had a 0.303% (95% CI 0.261-0.346) suicide death rate over the following 90 days. Model performance was similar across subgroups defined by age, sex, race, and ethnicity.
conclusionsMachine learning models using coded data from health records have moderate performance in predicting suicide death following emergency department visits for mental health or self-harm diagnosis and could be used to identify patients needing more systematic follow-up.
Indexed as
Identifiers
What OpenQuestion holds
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.