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Identifying long-term and imminent suicide predictors in a general population and a clinical sample with machine learning
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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
BMC Psychiatry, Volume: 22, Issue: 1, Start page: 120
Swansea University Authors:
Lloyd Balbuena , Ann John
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DOI (Published version): 10.1186/s12888-022-03702-y
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...
| Published in: | BMC Psychiatry |
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| ISSN: | 1471-244X |
| Published: |
Springer Science and Business Media LLC
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa59416 |
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| 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. |
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| Keywords: |
suicide; machine learning; prediction; primary prevention; secondary prevention |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
This research was supported by grants to the first author from the Department of Psychiatry, University of Saskatchewan, the Saskatchewan Health Research Foundation, the Royal University Hospital Foundation Community Mental Health Fund, the Google Cloud Platform, and Compute Canada. |
| Issue: |
1 |
| Start Page: |
120 |