Identifying long-term and imminent suicide predictors in a general population and a clinical sample with machine learning

BMC Psychiatry

Marilyn Baetz¹  Cindy Xin Feng²  Anne Lise Brantsæter³  Arash Shamloo⁴  Kerstina Boctor⁴  Lloyd D. Balbuena⁴  Joseph Andrew Sexton⁵  Hemant Ishwaran⁶  Candace LaPointe⁷  Douglas Harder⁷  Elizabeth Letwiniuk⁷  Ann John⁸ 

[1] College of Medicine, University of Saskatchewan, Saskatoon, Canada;Department of Community Health and Epidemiology, Dalhousie University, Halifax, Canada;Department of Environmental Health, Norwegian Institute of Public Health, Oslo, Norway;Department of Psychiatry, University of Saskatchewan, Saskatoon, Canada;Diakonhjemmet Hospital, Oslo, Norway;Division of Biostatistics, University of Miami, Miami, USA;Mental Health & Addictions Services, Saskatchewan Health Authority, Saskatoon, Canada;Swansea University Medical School, Swansea University, Swansea, United Kingdom;

关键词: suicide;    machine learning;    prediction;    primary prevention;    secondary prevention;   

DOI  :  10.1186/s12888-022-03702-y

来源: Springer

【 摘 要 】

BackgroundMachine learning (ML) is increasingly used to predict suicide deaths but their value for suicide prevention has not been established. Our first objective was to identify risk and protective factors in a general population. Our second objective was to identify factors indicating imminent suicide risk.MethodsWe used survival and ML models to identify lifetime predictors using the Cohort of Norway (n=173,275) and hospital diagnoses in a Saskatoon clinical sample (n=12,614). The mean follow-up times were 17 years and 3 years for the Cohort of Norway and Saskatoon respectively. People in the clinical sample had a longitudinal record of hospital visits grouped in six-month intervals. We developed models in a training set and these models predicted survival probabilities in held-out test data.ResultsIn the general population, we found that a higher proportion of low-income residents in a county, mood symptoms, and daily smoking increased the risk of dying from suicide in both genders. In the clinical sample, the only predictors identified were male gender and older age.ConclusionSuicide prevention probably requires individual actions with governmental incentives. The prediction of imminent suicide remains highly challenging, but machine learning can identify early prevention targets.

【 授权许可】

CC BY