View in EDS HTML Full Text PDF Full Text

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

Saved in:
Bibliographic Details
Title: Using machine‐learning methods to identify early‐life predictors of 11‐year language outcome.
Authors: Gasparini, Loretta, Shepherd, Daisy A., Bavin, Edith L., Eadie, Patricia, Reilly, Sheena, Morgan, Angela T., Wake, Melissa
Source: Journal of Child Psychology & Psychiatry. Aug2023, Vol. 64 Issue 8, p1242-1252. 11p. 3 Charts.
Subjects: Confidence intervals, Machine learning, Health outcome assessment, Speech evaluation, Risk assessment, Surveys, Language acquisition, Questionnaires, Descriptive statistics, Prediction models, Sensitivity & specificity (Statistics), Language disorders, Early diagnosis, Parents, Longitudinal method, Disease risk factors
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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Child Psychology & Psychiatry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 164763223
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Using machine‐learning methods to identify early‐life predictors of 11‐year language outcome.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Gasparini%2C+Loretta%22">Gasparini, Loretta</searchLink><br /><searchLink fieldCode="AR" term="%22Shepherd%2C+Daisy+A%2E%22">Shepherd, Daisy A.</searchLink><br /><searchLink fieldCode="AR" term="%22Bavin%2C+Edith+L%2E%22">Bavin, Edith L.</searchLink><br /><searchLink fieldCode="AR" term="%22Eadie%2C+Patricia%22">Eadie, Patricia</searchLink><br /><searchLink fieldCode="AR" term="%22Reilly%2C+Sheena%22">Reilly, Sheena</searchLink><br /><searchLink fieldCode="AR" term="%22Morgan%2C+Angela+T%2E%22">Morgan, Angela T.</searchLink><br /><searchLink fieldCode="AR" term="%22Wake%2C+Melissa%22">Wake, Melissa</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Child+Psychology+%26+Psychiatry%22">Journal of Child Psychology & Psychiatry</searchLink>. Aug2023, Vol. 64 Issue 8, p1242-1252. 11p. 3 Charts.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Health+outcome+assessment%22">Health outcome assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+evaluation%22">Speech evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Surveys%22">Surveys</searchLink><br /><searchLink fieldCode="DE" term="%22Language+acquisition%22">Language acquisition</searchLink><br /><searchLink fieldCode="DE" term="%22Questionnaires%22">Questionnaires</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+%26+specificity+%28Statistics%29%22">Sensitivity & specificity (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Language+disorders%22">Language disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Early+diagnosis%22">Early diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Parents%22">Parents</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+risk+factors%22">Disease risk factors</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Child Psychology & Psychiatry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=164763223
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/jcpp.13733
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 1242
    Subjects:
      – SubjectFull: Confidence intervals
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Health outcome assessment
        Type: general
      – SubjectFull: Speech evaluation
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Surveys
        Type: general
      – SubjectFull: Language acquisition
        Type: general
      – SubjectFull: Questionnaires
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Sensitivity & specificity (Statistics)
        Type: general
      – SubjectFull: Language disorders
        Type: general
      – SubjectFull: Early diagnosis
        Type: general
      – SubjectFull: Parents
        Type: general
      – SubjectFull: Longitudinal method
        Type: general
      – SubjectFull: Disease risk factors
        Type: general
    Titles:
      – TitleFull: Using machine‐learning methods to identify early‐life predictors of 11‐year language outcome.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Gasparini, Loretta
      – PersonEntity:
          Name:
            NameFull: Shepherd, Daisy A.
      – PersonEntity:
          Name:
            NameFull: Bavin, Edith L.
      – PersonEntity:
          Name:
            NameFull: Eadie, Patricia
      – PersonEntity:
          Name:
            NameFull: Reilly, Sheena
      – PersonEntity:
          Name:
            NameFull: Morgan, Angela T.
      – PersonEntity:
          Name:
            NameFull: Wake, Melissa
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Text: Aug2023
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 00219630
          Numbering:
            – Type: volume
              Value: 64
            – Type: issue
              Value: 8
          Titles:
            – TitleFull: Journal of Child Psychology & Psychiatry
              Type: main
ResultId 1