Using machine-learning methods to identify early-life predictors of 11-year language outcome

Loretta Gasparini; Daisy A Shepherd; Edith L Bavin; Patricia Eadie; Sheena Reilly; Angela T Morgan; Melissa Wake

doi:10.1111/jcpp.13733

Using machine-learning methods to identify early-life predictors of 11-year language outcome

J Child Psychol Psychiatry. 2023 Aug;64(8):1242-1252. doi: 10.1111/jcpp.13733. Epub 2022 Dec 7.

Authors

Loretta Gasparini^{1

2}, Daisy A Shepherd^{1

2}, Edith L Bavin^{1

3}, Patricia Eadie⁴, Sheena Reilly^{1

2

5}, Angela T Morgan^{1

6

7}, Melissa Wake^{1

2

8}

Affiliations

¹ Murdoch Children's Research Institute, Parkville, VIC, Australia.
² Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia.
³ School of Psychology and Public Health, La Trobe University, Bundoora, VIC, Australia.
⁴ Melbourne Graduate School of Education, The University of Melbourne, Parkville, VIC, Australia.
⁵ Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia.
⁶ Department of Audiology and Speech Pathology, The University of Melbourne, Parkville, VIC, Australia.
⁷ Royal Children's Hospital Melbourne, Parkville, VIC, Australia.
⁸ Liggins Institute, The University of Auckland, Auckland, New Zealand.

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 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.

Keywords: Language development; language disorders; longitudinal studies; machine learning; sensitivity and specificity.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Child
Child Language
Child, Preschool
Humans
Parent-Child Relations
Parents*
Quality of Life*
Vocabulary