A biological phenotype of suicide attempt in adolescents with nonsuicidal self-injury: a machine-based learning approach.

ABSTRACT: Suicide attempts (SA) are a common risk in adolescents with non-suicidal self-injury (NSSI). In the present study, we investigated whether a set of biological markers contributed (above clinical features) to the distinction of adolescents with NSSI and SA from those with NSSI alone using machine-based learning approaches. Female adolescents engaging in NSSI (n = 161) were recruited from our outpatient clinic for risk-taking and self-harming behavior (AtR!Sk). Different machine-based learning models (logistic regression, elastic net regression, random forests, gradient boosted trees) with repeated cross-validation were applied. We tested whether a) the full set of neurobiological markers, b) a reduced set including preselected markers based on existing evidence (CRP, interleukin-6, salivary

SUBMITTER: Fink E 

PROVIDER: S-EPMC12518856 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature