Predicting Sex-Specific Nonfatal Suicide Attempt Risk Using Machine Learning and Data From Danish National Registries

Abstract

Suicide attempts are a leading cause of injury globally. Accurate prediction of suicide attempts might 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 nonfatal 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 demographic factors, 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 nonfatal suicide attempts, while consistency between our findings and previous work in this area adds to the call to move machine learning suicide research toward the examination of high-risk subpopulations.

Keywords: Denmark; National Registry; machine learning; prediction; suicide attempts.

Figure 1

Classification tree depicting suicide attempt predictors among men in Denmark, 1995–2015. Poisoning refers to poisoning by, adverse effect of, and underdosing of drugs, medicaments, and biological substances. Drugs refers to drugs used in additive disorders. AD, adjustment disorders; RSS, reaction to severe stress.

Figure 2

Classification tree depicting suicide attempt predictors among women in Denmark, 1995–2015. Poisoning refers to poisoning by, adverse effect of and underdosing of drugs, medicaments, and biological substances. The referent of income quartile 2 is income quartile 1. AD, adjustment disorders; MDD, major depressive disorder; RSS, reaction to severe stress.

Figure 3

Variable importance to suicide attempt prediction accuracy among men in Denmark, 1995–2015. The black dots represent the mean decrease in accuracy (MDA) value in fold 1, and the gray dots represent the MDA value in fold 2. The predictors that were in the top 30 predictors in folds 1 and 2 for men are shown in bold. The reference group for age ≤14 years is income quartile 1. The reference group for state pension is employed. Poisoning refers to poisoning by, adverse effect, of and underdosing of drugs, medicaments, and biological substances. GORD, drugs for peptic ulcer and gastro-esophageal reflux disease; RSS, reaction to severe stress.

Figure 4

Variable importance to suicide attempt prediction accuracy among women in Denmark, 1995–2015. The black dots represent the mean decrease in accuracy (MDA) value in fold 1, and the gray dots represent the MDA value in fold 2. The predictors that were in the top 30 predictors in folds 1 and 2 for women are shown in bold. The reference group for age ≤14 years is income quartile 1. The reference group for state pension is employed. Poisoning refers to poisoning by, adverse effect of, and underdosing of drugs, medicaments, and biological substances. GORD, drugs for peptic ulcer and gastro-esophageal reflux disease; RSS, reaction to severe stress.