ArticleThe British journal of psychiatry : the journal of mental science2021
Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark.
Article in The British journal of psychiatry : the journal of mental science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 4 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 4 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- 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
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- The Role of Socioeconomic Position in the Association Between Mental Disorders and Mortality: A Systematic Review and Meta-Analysis.JAMA psychiatry · 2024Pooled it
- Machine Learning Identifies High-Risk Suicide Profiles in a Population-Based Forensic Registry.Diagnostics (Basel, Switzerland) · 2026Article
- Prediction of self-harm in people with newly-diagnosed depression: development and validation of risk prediction models.Molecular psychiatry · 2026Article
- Joint models targeting U.S. Army soldiers at high-risk of post-separation unemployment, homelessness, and suicide-related behaviors.Npj mental health research · 2026Article
- Predicting Firearm Suicide among US Army Veterans Transitioning from Active Service.Nature. Mental health · 2026Article
- Identifying most important predictors for suicidal thoughts and behaviours among healthcare workers active during the Spain COVID-19 pandemic: a machine-learning approach.Epidemiology and psychiatric sciences · 2025Article
- Precision psychiatry needs causal inference.Acta neuropsychiatrica · 2024Review
- Premature Death, Suicide, and Nonlethal Intentional Self-Harm After Psychiatric Discharge.JAMA network open · 2024Article
- Risk Prediction Model for Non-Suicidal Self-Injury in Chinese Adolescents with Major Depressive Disorder Based on Machine Learning.Neuropsychiatric disease and treatment · 2024Article
- Risk of suicide attempts and self-harm after 1.4 million general medical hospitalizations of men with mental illness.Journal of psychiatric research · 2023Article
- Predictors of nonfatal suicide attempts within 30 days of discharge from psychiatric hospitalization: Sex-specific models developed using population-based registries.Journal of affective disorders · 2022Article
- Suicide prediction among men and women with depression: A population-based study.Journal of psychiatric research · 2021Article
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Authors and funding
8 authors at 6 institutions in 2 countries.
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Abstract
backgroundSuicide risk is high in the 30 days after discharge from psychiatric hospital, but knowledge of the profiles of high-risk patients remains limited.
aimsTo examine sex-specific risk profiles for suicide in the 30 days after discharge from psychiatric hospital, using machine learning and Danish registry data.
methodWe conducted a case-cohort study capturing all suicide cases occurring in the 30 days after psychiatric hospital discharge in Denmark from 1 January 1995 to 31 December 2015 (n = 1205). The comparison subcohort was a 5% random sample of all persons born or residing in Denmark on 1 January 1995, and who had a first psychiatric hospital admission between 1995 and 2015 (n = 24 559). Predictors included diagnoses, surgeries, prescribed medications and demographic information. The outcome was suicide death recorded in the Danish Cause of Death Registry.
resultsFor men, prescriptions for anxiolytics and drugs used in addictive disorders interacted with other characteristics in the risk profiles (e.g. alcohol-related disorders, hypnotics and sedatives) that led to higher risk of postdischarge suicide. In women, there was interaction between recurrent major depression and other characteristics (e.g. poisoning, low income) that led to increased risk of suicide. Random forests identified important suicide predictors: alcohol-related disorders and nicotine dependence in men and poisoning in women.
conclusionsOur findings suggest that accurate prediction of suicide during the high-risk period immediately after psychiatric hospital discharge may require a complex evaluation of multiple factors for men and women.
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