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

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 probabiliti

SUBMITTER: Balbuena LD 

PROVIDER: S-EPMC8848909 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature