Psychopathology profiles and longitudinal correlates of nonsuicidal self-injury in youth: a machine-learning approach.

ABSTRACT: Nonsuicidal self-injury (NSSI) in youth is clinically heterogeneous. We aimed to identify distinct psychopathology-based profiles among children and adolescents reporting NSSI and their longitudinal correlates. Participants (N = 1 345) were drawn from the Brazilian High-Risk Cohort Study, which conducted extensive phenotypic assessments at baseline (ages 6-14 years) and across two follow-up waves (ages 9-18 and 13-23 years). First, we applied unsupervised machine-learning algorithms (Self-Organizing Maps and k-means clustering) to identify distinct psychopathology-based profiles among youth reporting NSSI at the second follow-up. We then employed three models to identify longitudinal predictors of these profiles: logistic regression, elastic net, and random forest. Analyses revealed two di

SUBMITTER: Croci MS 

PROVIDER: S-EPMC12923860 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature