A machine learning approach for predicting suicidal thoughts and behaviours among college students.

ABSTRACT: Suicidal thoughts and behaviours are prevalent among college students. Yet little is known about screening tools to identify students at higher risk. We aimed to develop a risk algorithm to identify the main predictors of suicidal thoughts and behaviours among college students within one-year of baseline assessment. We used data collected in 2013-2019 from the French i-Share cohort, a longitudinal population-based study including 5066 volunteer students. To predict suicidal thoughts and behaviours at follow-up, we used random forests models with 70 potential predictors measured at baseline, including sociodemographic and familial characteristics, mental health and substance use. Model performance was measured using the area under the receiver operating curve (AUC), sensitivity, and positiv

SUBMITTER: Macalli M 

PROVIDER: S-EPMC8206419 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature