ArticleScientific reports2021
Moving towards accurate and early prediction of language delay with network science and machine learning approaches.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Low language capacity in childhood: A systematic review of prevalence estimates.International journal of language & communication disordersPooled it
- Application of machine learning in early childhood development research: a scoping review.BMJ open · 2025Article
- [Artificial intelligence in preventive medicine for children and adolescents-applications and acceptance].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Review
- Using machine-learning methods to identify early-life predictors of 11-year language outcome.Journal of child psychology and psychiatry, and allied disciplines · 2023Article
- Sensitivity to Semantic Relationships in U.S. Monolingual English-Speaking Typical Talkers and Late Talkers.Journal of speech, language, and hearing research : JSLHR · 2023Article
- Two pathways in vocabulary development: Large-scale differences in noun and verb semantic structure.Cognitive psychology · 2023Article
- Drivers of Lexical Processing and Implications for Early Learning.Annual review of developmental psychology · 2022Article
- Lexico-semantic structure in vocabulary and its links to lexical processing in toddlerhood and language outcomes at age three.Developmental psychology · 2022Article
- ASD-Time for a paradigm shift.Frontiers in psychiatry · 2022Article
- Article
- Cognitive Networks for Knowledge Modeling: A Gentle Introduction for Data- and Cognitive Scientists.Wiley interdisciplinary reviews. Cognitive scienceReview
- Do parent-reported early indicators predict later developmental language disorder? A Raine Study investigation.International journal of language & communication disordersArticle
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Authors and funding
3 authors.
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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.
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