Moving towards accurate and early prediction of language delay with network science and machine learning approaches.

ABSTRACT: Due to wide variability of typical language development, it has been historically difficult to distinguish typical and delayed trajectories of early language growth. Improving our understanding of factors that signal language disorder and delay has the potential to improve the lives of the millions with developmental language disorder (DLD). We develop predictive models of low language (LL) outcomes by analyzing parental report measures of early language skill using machine learning and network science approaches. We harmonized two longitudinal datasets including demographic and standardized measures of early language skills (the MacArthur-Bates Communicative Developmental Inventories; MBCDI) as well as a later measure of LL. MBCDI data was used to calculate several graph-theoretic measure

SUBMITTER: Borovsky A 

PROVIDER: S-EPMC8047042 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature