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...

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Autores principales:	Arielle Borovsky, Donna Thal, Laurence B. Leonard
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ea0a23d7ccb348038a902a296973c3c5
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spelling	oai:doaj.org-article:ea0a23d7ccb348038a902a296973c3c52021-12-02T14:27:53ZMoving towards accurate and early prediction of language delay with network science and machine learning approaches10.1038/s41598-021-85982-02045-2322https://doaj.org/article/ea0a23d7ccb348038a902a296973c3c52021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85982-0https://doaj.org/toc/2045-2322Abstract 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 measures of lexico-semantic structure in toddlers’ expressive vocabularies. We use machine-learning techniques to construct predictive models with these datasets to identify toddlers who will have later LL outcomes at preschool and school-age. This approach yielded robust and reliable predictions of later LL outcome with classification accuracies in single datasets exceeding 90%. Generalization performance between different datasets was modest due to differences in outcome ages and diagnostic measures. Grammatical and lexico-semantic measures ranked highly in predictive classification, highlighting promising avenues for early screening and delineating the roots of language disorders.Arielle BorovskyDonna ThalLaurence B. LeonardNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Arielle Borovsky Donna Thal Laurence B. Leonard Moving towards accurate and early prediction of language delay with network science and machine learning approaches
description	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 measures of lexico-semantic structure in toddlers’ expressive vocabularies. We use machine-learning techniques to construct predictive models with these datasets to identify toddlers who will have later LL outcomes at preschool and school-age. This approach yielded robust and reliable predictions of later LL outcome with classification accuracies in single datasets exceeding 90%. Generalization performance between different datasets was modest due to differences in outcome ages and diagnostic measures. Grammatical and lexico-semantic measures ranked highly in predictive classification, highlighting promising avenues for early screening and delineating the roots of language disorders.
format	article
author	Arielle Borovsky Donna Thal Laurence B. Leonard
author_facet	Arielle Borovsky Donna Thal Laurence B. Leonard
author_sort	Arielle Borovsky
title	Moving towards accurate and early prediction of language delay with network science and machine learning approaches
title_short	Moving towards accurate and early prediction of language delay with network science and machine learning approaches
title_full	Moving towards accurate and early prediction of language delay with network science and machine learning approaches
title_fullStr	Moving towards accurate and early prediction of language delay with network science and machine learning approaches
title_full_unstemmed	Moving towards accurate and early prediction of language delay with network science and machine learning approaches
title_sort	moving towards accurate and early prediction of language delay with network science and machine learning approaches
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ea0a23d7ccb348038a902a296973c3c5
work_keys_str_mv	AT arielleborovsky movingtowardsaccurateandearlypredictionoflanguagedelaywithnetworkscienceandmachinelearningapproaches AT donnathal movingtowardsaccurateandearlypredictionoflanguagedelaywithnetworkscienceandmachinelearningapproaches AT laurencebleonard movingtowardsaccurateandearlypredictionoflanguagedelaywithnetworkscienceandmachinelearningapproaches
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Moving towards accurate and early prediction of language delay with network science and machine learning approaches

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