Machine Learning Assessment of Early Life Factors Predicting Suicide Attempt in Adolescence or Young Adulthood.

ABSTRACT:

Importance

Although longitudinal studies have reported associations between early life factors (ie, in-utero/perinatal/infancy) and long-term suicidal behavior, they have concentrated on 1 or few selected factors, and established associations, but did not investigate if early-life factors predict suicidal behavior.

Objective

To identify and evaluate the ability of early-life factors to predict suicide attempt in adolescents and young adults from the general population.

Design, setting, and participants

This prognostic study used data from the Québec Longitudinal Study of Child Development, a population-based longitudinal study from Québec province, Canada. Participants were followed-up from birth to age 20 years. Random forest classification algorithms were developed

SUBMITTER: Navarro MC 

PROVIDER: S-EPMC7955274 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature