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

Lloyd D. Balbuena, Marilyn Baetz, Joseph Andrew Sexton, Douglas Harder, Cindy Xin Feng, Kerstina Boctor, Candace LaPointe, Elizabeth Letwiniuk, Arash Shamloo, Hemant Ishwaran, Ann John, Anne Lise Brantsæter

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Machine 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. Methods: We 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. Results: In 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. Conclusion: Suicide prevention probably requires individual actions with governmental incentives. The prediction of imminent suicide remains highly challenging, but machine learning can identify early prevention targets.

Original languageEnglish (US)
Article number120
JournalBMC Psychiatry
Volume22
Issue number1
DOIs
StatePublished - Dec 2022
Externally publishedYes

Keywords

  • machine learning
  • prediction
  • primary prevention
  • secondary prevention
  • suicide

ASJC Scopus subject areas

  • Psychiatry and Mental health

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