ArticleBJPsych open2021
Meta-analysis of the strength of exploratory suicide prediction models; from clinicians to computers.
Article in BJPsych open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 7 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Prediction models for self-harm and suicide: a systematic review and critical appraisal.BMC medicine · 2025Pooled it
- Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Suicidal risk in patients with aggression in schizophrenia: a systematic review.Frontiers in psychiatry · 2025Pooled it
- Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies.BMC medical informatics and decision making · 2024Pooled 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
- Monitoring Within-Person Dynamics in Adolescents Hospitalized for Acute Suicidal Distress: Protocol for an Intensive Longitudinal Study.JMIR research protocols · 2026Article
- Scalable prediction of suicidal risk in university students: a three steps machine learning approach in university settings.Scientific reports · 2026Article
- Integrating neurobiological markers to prospectively predict adolescent non-suicidal self-injury and suicide attempts: a machine learning approach.Child and adolescent psychiatry and mental health · 2026Article
- Diagnostic accuracy of machine learning approaches for suicide‑related outcomes: a meta‑analysis.Annals of general psychiatry · 2026Article
- Evaluating the role of cost and fairness in machine learning models of suicide risk.Scientific reports · 2026Article
- Article
- Considerations for evaluating the practical utility of machine learning in suicide risk estimation: the role of cost and equity.Research square · 2025Article
- The descent of agamemnon and the disquietude of job: The death of agency as the spur of suicide.Psychological review · 2025Review
- A biological phenotype of suicide attempt in adolescents with nonsuicidal self-injury: a machine-based learning approach.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Article
- Using a fine-tuned large language model for symptom-based depression evaluation.NPJ digital medicine · 2025Article
- Review: Suicide and its relationship to aggression and impulsivity.Cognitive, affective & behavioral neuroscience · 2025Review
- The Etiopathogenic Mosaic of Suicidal Behaviour.Behavioral sciences (Basel, Switzerland) · 2025Review
- Navigating extreme class imbalance in suicide risk prediction.Frontiers in psychiatry · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSuicide prediction models have been formulated in a variety of ways and are heterogeneous in the strength of their predictions. Machine learning has been a proposed as a way of improving suicide predictions by incorporating more suicide risk factors.
aimsTo determine whether machine learning and the number of suicide risk factors included in suicide prediction models are associated with the strength of the resulting predictions.
methodRandom-effect meta-analysis of exploratory suicide prediction models constructed by combining two or more suicide risk factors or using clinical judgement (Prospero Registration CRD42017059665). Studies were located by searching for papers indexed in PubMed before 15 August 2020 with the term suicid* in the title.
resultsIn total, 86 papers reported 102 suicide prediction models and included 20 210 411 people and 106 902 suicides. The pooled odds ratio was 7.7 (95% CI 6.7-8.8) with high between-study heterogeneity (I2 = 99.5). Machine learning was associated with a non-significantly higher odds ratio of 11.6 (95% CI 6.0-22.3) and clinical judgement with a non-significantly lower odds ratio of 4.7 (95% CI 2.1-10.9). Models including a larger number of suicide risk factors had a higher odds ratio when machine-learning studies were included (P = 0.02). Among non-machine-learning studies, suicide prediction models including fewer risk factors performed just as well as those including more risk factors.
conclusionsMachine learning might have the potential to improve the performance of suicide prediction models by increasing the number of included suicide risk factors but its superiority over other methods is unproven.
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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.