Applied machine learning to identify differential risk groups underlying externalizing and internalizing problem behaviors trajectories: A case study using a cohort of Asian American children.

ABSTRACT:

Background

Internalizing and externalizing problems account for over 75% of the mental health burden in children and adolescents in the US, with higher burden among minority children. While complex interactions of multilevel factors are associated with these outcomes and may enable early identification of children in higher risk, prior research has been limited by data and application of traditional analysis methods. In this case example focused on Asian American children, we address the gap by applying data-driven statistical and machine learning methods to study clusters of mental health trajectories among children, investigate optimal predictions of children at high-risk cluster, and identify key early predictors.

Methods

Data from the US Early Childhood Longitudinal Stud

SUBMITTER: Adhikari S 

PROVIDER: S-EPMC9983857 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature