Predicting Sex-Specific Non-Fatal Suicide Attempt Risk Using Machine Learning and Data from Danish National Registries.

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.