ArticleBMC psychiatry2014
Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments.
Article in BMC psychiatry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 6 of them syntheses that pooled it.
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Who cites it
62 citing papers in PubMed, 6 syntheses or guidelines pooled it, 152 citations in OpenAlex.
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 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
- Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.Schizophrenia bulletin · 2021Pooled it
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2017Pooled it
- Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.Journal of medical Internet research · 2016Guideline
- Artificial Intelligence in Psychiatry: Five Decades of Progress and Persistent Translational Challenges.Psychiatric research and clinical practice · 2026Article
- Comparing Risk Prediction for Suicide Attempts and Deaths After Emergency Department Visits for Individuals With Mental Health Disorders.The Journal of clinical psychiatry · 2026Article
- Development and validation of the suicide risk score: a novel suicide risk prediction tool for patients with end-stage kidney disease.Clinical kidney journal · 2026Article
- Predictive models for active suicidal ideation in cognitive decline: identifying risk factors.BMC psychiatry · 2026Article
- Suicide attempt risk in autism: a national EHR study of 2.3 million individuals.Frontiers in psychiatry · 2026Article
- Predicting suicide death among veterans after psychiatric hospitalization using transformer based models with social determinants and NLP.Scientific reports · 2025Article
- Screening and Risk Algorithms for Detecting Pediatric Suicide Risk in the Emergency Department.JAMA network open · 2025Article
- Evaluating Generative Pretrained Transformer (GPT) models for suicide risk assessment in synthetic patient journal entries.BMC psychiatry · 2025Article
- 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
- Research themes and key data points for child and adolescent emergency department mental health presentations: A national Delphi study.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2025Article
- Article
- Validation of the Colombian-Spanish Suicidality Scale for Screening Suicide Risk in Clinical and Community Settings.Journal of clinical medicine · 2024Article
- Impact of Implementation Facilitation on the REACH VET Clinical Program for Veterans at Risk for Suicide.Psychiatric services (Washington, D.C.) · 2024Article
- Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records.Translational psychiatry · 2024Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo date, our ability to accurately identify patients at high risk from suicidal behaviour, and thus to target interventions, has been fairly limited. This study examined a large pool of factors that are potentially associated with suicide risk from the comprehensive electronic medical record (EMR) and to derive a predictive model for 1-6 month risk.
methods7,399 patients undergoing suicide risk assessment were followed up for 180 days. The dataset was divided into a derivation and validation cohorts of 4,911 and 2,488 respectively. Clinicians used an 18-point checklist of known risk factors to divide patients into low, medium, or high risk. Their predictive ability was compared with a risk stratification model derived from the EMR data. The model was based on the continuation-ratio ordinal regression method coupled with lasso (which stands for least absolute shrinkage and selection operator).
resultsIn the year prior to suicide assessment, 66.8% of patients attended the emergency department (ED) and 41.8% had at least one hospital admission. Administrative and demographic data, along with information on prior self-harm episodes, as well as mental and physical health diagnoses were predictive of high-risk suicidal behaviour. Clinicians using the 18-point checklist were relatively poor in predicting patients at high-risk in 3 months (AUC 0.58, 95% CIs: 0.50 - 0.66). The model derived EMR was superior (AUC 0.79, 95% CIs: 0.72 - 0.84). At specificity of 0.72 (95% CIs: 0.70-0.73) the EMR model had sensitivity of 0.70 (95% CIs: 0.56-0.83).
conclusionPredictive models applied to data from the EMR could improve risk stratification of patients presenting with potential suicidal behaviour. The predictive factors include known risks for suicide, but also other information relating to general health and health service utilisation.
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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.