Using machine-learning methods to identify early-life predictors of 11-year language outcome
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Shepherd, DA
Bavin, EL
Eadie, P
Reilly, S
Morgan, AT
Wake, M
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Abstract
Background: Language is foundational for neurodevelopment and quality of life, but an estimated 10% of children have a language disorder at age 5. Many children shift between classifications of typical and low language if assessed at multiple times in the early years, making it difficult to identify which children will have persisting difficulties and benefit most from support. This study aims to identify a parsimonious set of preschool indicators that predict language outcomes in late childhood, using data from the population-based Early Language in Victoria Study (n = 839). Methods: Parents completed surveys about their children at ages 8, 12, 24, and 36 months. At 11 years, children were assessed using the Clinical Evaluation of Language Fundamentals 4th Edition (CELF-4). We used random forests to identify which of the 1990 parent-reported questions best predict children's 11-year language outcome (CELF-4 score ≤81 representing low language) and used SuperLearner to estimate the accuracy of the constrained sets of questions. Results: At 24 months, seven predictors relating to vocabulary, symbolic play, pragmatics and motor skills yielded 73% sensitivity (95% CI: 57, 85) and 77% specificity (95% CI: 74, 80) for predicting low language at 11 years. At 36 months, 7 predictors relating to morphosyntax, vocabulary, parent–child interactions, and parental stress yielded 75% sensitivity (95% CI: 58, 88) and 85% specificity (95% CI: 81, 87). Measures at 8 and 12 months yielded unsatisfactory accuracy. Conclusions: We identified two short sets of questions that predict language outcomes at age 11 with fair accuracy. Future research should seek to replicate results in a separate cohort.
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Journal of Child Psychology and Psychiatry and Allied Disciplines
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© 2022 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Applied linguistics and educational linguistics
Language development
language disorders
longitudinal studies
machine learning
sensitivity and specificity
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Gasparini, L; Shepherd, DA; Bavin, EL; Eadie, P; Reilly, S; Morgan, AT; Wake, M, Using machine-learning methods to identify early-life predictors of 11-year language outcome, Journal of Child Psychology and Psychiatry and Allied Disciplines, 2022