Age-stratified predictions of suicide attempts using machine learning in middle and late adolescence.

ABSTRACT

BACKGROUND: Prevalence of suicidal behaviour increases rapidly in middle to late adolescence. Predicting suicide attempts across different ages would enhance our understanding of how suicidal behaviour manifests in this period of rapid development. This study aimed to develop separate models to predict suicide attempts within a cohort at middle and late adolescence. It also sought to examine differences between the models derived across both developmental stages. METHODS: This study used data from the nationally representative Longitudinal Study of Australian Children (N = 2266). We selected over 700 potential suicide attempt predictors measured via self-report questionnaires, and linked healthcare and education administrative datasets. Logistic regression, random forests, and gradient boosting algorithms were developed to predict suicide attempts across two stages (mid-adolescence 14-15 years; late adolescence 18-19 years) using predictors sampled two years prior (mid-adolescence 12-13 years; late adolescence 16-17 years). RESULTS: The late adolescence models (AUROC = 0.77-0.88, F1-score = 0.22-0.28, Sensitivity = 0.54-0.64) performed better than the mid-adolescence models (AUROC = 0.70-0.76, F1-score = 0.12-0.19, Sensitivity = 0.40-0.64). The most important features for predicting suicide attempts in mid-adolescence were mostly school-related, while the most important features in late adolescence included measures of prior suicidality, psychosocial health, and future plans. CONCLUSIONS: To date, this is the first study to use machine learning models to predict suicide attempts at different ages. Our findings suggest that the optimal suicide risk prediction model differs by stage of adolescence. Future research and interventions should consider that risk presentations can change rapidly during adolescence.