Using machine-learning methods to identify early-life predictors of 11-year language outcome / Loretta GASPARINI in Journal of Child Psychology and Psychiatry, 64-8 (August 2023)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 64-8 (August 2023) . - p.1242-1252 Titre : Using machine-learning methods to identify early-life predictors of 11-year language outcome Type de document : Texte imprimé et/ou numérique Auteurs : Loretta GASPARINI, Auteur ; Daisy A. SHEPHERD, Auteur ; Edith L. BAVIN, Auteur ; Patricia EADIE, Auteur ; Sheena REILLY, Auteur ; Angela T. MORGAN, Auteur ; Melissa WAKE, Auteur Article en page(s) : p.1242-1252 Langues : Anglais (eng) Mots-clés : Language development  language disorders  sensitivity and specificity  longitudinal studies  machine learning Index. décimale : PER Périodiques Résumé : 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 behavior yielded 73% sensitivity (95% CI: 57, 85) and 77% specificity (95% CI: 74, 80) for predicting low language at 11 years. [Corrections made on 5 May 2023, after first online publication: In the preceding sentence motor skills has been corrected to behavior in this version.] 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. En ligne : https://doi.org/10.1111/jcpp.13733 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=508 [article] Using machine-learning methods to identify early-life predictors of 11-year language outcome [Texte imprimé et/ou numérique] / Loretta GASPARINI, Auteur ; Daisy A. SHEPHERD, Auteur ; Edith L. BAVIN, Auteur ; Patricia EADIE, Auteur ; Sheena REILLY, Auteur ; Angela T. MORGAN, Auteur ; Melissa WAKE, Auteur . - p.1242-1252. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 64-8 (August 2023) . - p.1242-1252 Mots-clés : Language development  language disorders  sensitivity and specificity  longitudinal studies  machine learning Index. décimale : PER Périodiques Résumé : 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 behavior yielded 73% sensitivity (95% CI: 57, 85) and 77% specificity (95% CI: 74, 80) for predicting low language at 11 years. [Corrections made on 5 May 2023, after first online publication: In the preceding sentence motor skills has been corrected to behavior in this version.] 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. En ligne : https://doi.org/10.1111/jcpp.13733 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=508

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