ArticleEClinicalMedicine2020
Predicting death by suicide following an emergency department visit for parasuicide with administrative health care system data and machine learning.
Article in EClinicalMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 8 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 8 syntheses or guidelines pooled 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
- AI applications in disaster governance with health approach: A scoping review.Archives of public health = Archives belges de sante publique · 2025Pooled it
- Artificial intelligence-based risk assessment tools for sexual, reproductive and mental health: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- 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
- 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
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- Assessing the utility of health access data and social determinants of health in ecological suicide prediction models.Social psychiatry and psychiatric epidemiology · 2026Article
- Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.The annals of applied statistics · 2026Article
- Suicide attempt risk factors in individuals with psychiatric disorders and multi-affected families: a network analysis.Npj mental health research · 2026Article
- Conceptualization and development of the Maryland suicide data warehouse to improve mortality prediction.JAMIA open · 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
- Development and validation of a machine learning model to identify individuals at high risk for psychotic disorders using medical record data.BMC psychiatry · 2026Article
- Clinical guidelines on self-harm and suicide prevention: taking uncertainty into account in the evidence base.BMJ mental health · 2026Article
- RETRACTED: Artificial intelligence for emergency medical care.Health care science · 2025Review
- Article
- Importance of variables from different time frames for predicting self-harm using health system data.Journal of biomedical informatics · 2024Article
- Importance of variables from different time frames for predicting self-harm using health system data.medRxiv : the preprint server for health sciences · 2024Article
- Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records.BMC medical informatics and decision making · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSuicide is a leading cause of death worldwide and results in a large number of person years of life lost. There is an opportunity to evaluate whether administrative health care system data and machine learning can quantify suicide risk in a clinical setting.
methodsThe objective was to compare the performance of prediction models that quantify the risk of death by suicide within 90 days of an ED visit for parasuicide with predictors available in administrative health care system data.The modeling dataset was assembled from 5 administrative health care data systems. The data systems contained nearly all of the physician visits, ambulatory care visits, inpatient hospitalizations, and community pharmacy dispenses, of nearly the entire 4.07 million persons in Alberta, Canada. 101 predictors were selected, and these were assembled for each of the 8 quarters (2 years) prior to the quarter of death, resulting in 808 predictors in total for each person. Prediction model performance was validated with 10-fold cross-validation.
findingsThe optimal gradient boosted trees prediction model achieved promising discrimination (AUC: 0.88) and calibration that could lead to clinical applications. The 5 most important predictors in the optimal gradient boosted trees model each came from a different administrative health care data system.
interpretationThe combination of predictors from multiple administrative data systems and the combination of personal and ecologic predictors resulted in promising prediction performance. Further research is needed to develop prediction models optimized for implementation in clinical settings.
fundingThere was no funding for this study.
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