TY  - JOUR
PY  - 2021//
TI  - Predicting sex-specific non-fatal suicide attempt risk using machine learning and data from Danish national registries
JO  - American journal of epidemiology
A1  - Gradus, Jaimie L.
A1  - Rosellini, Anthony J.
A1  - Horváth-Puhó, Erzsébet
A1  - Jiang, Tammy
A1  - Street, Amy E.
A1  - Galatzer-Levy, Isaac
A1  - Lash, Timothy L.
A1  - Sørensen, Henrik T.
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - 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>
LA  - en
SN  - 0002-9262
UR  - http://dx.doi.org/10.1093/aje/kwab112
ID  - ref1
ER  -