@article{ref1,
title="Predicting sex-specific non-fatal suicide attempt risk using machine learning and data from Danish national registries",
journal="American journal of epidemiology",
year="2021",
author="Gradus, Jaimie L. and Rosellini, Anthony J. and Horváth-Puhó, Erzsébet and Jiang, Tammy and Street, Amy E. and Galatzer-Levy, Isaac and Lash, Timothy L. and Sørensen, Henrik T.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Suicide attempts are a leading cause of injury globally. Accurate prediction of suicide attempts may offer opportunities for prevention. This case-cohort study used machine learning to examine sex-specific risk profiles for suicide attempts in Danish nationwide registry data. Cases were all persons who made a non-fatal suicide attempt between 1995 and 2015 (n = 22,974); the subcohort was a 5% random sample of the population at risk on January 1, 1995 (n = 265,183). We developed sex-stratified classification trees and random forests using 1,458 predictors including demographics, family histories, psychiatric and physical health diagnoses, surgery, and prescribed medications. We found that substance use disorders/treatment, prescribed psychiatric medications, previous poisoning diagnoses, and stress disorders were important factors for predicting suicide attempts among men and women. Individuals in the top 5% of predicted risk accounted for 44.7% of all suicide attempts among men and 43.2% of all attempts among women. Our findings illuminate novel risk factors and interactions that are most predictive of non-fatal suicide attempts, while consistency between our findings and previous work in this area adds to the call to move machine learning suicide research towards the examination of high-risk subpopulations.<p /> <p>Language: en</p>",
language="en",
issn="0002-9262",
doi="10.1093/aje/kwab112",
url="http://dx.doi.org/10.1093/aje/kwab112"
}