Predicting Language Difficulties in Middle Childhood From Early Developmental Milestones: A Comparison of Traditional Regression and Machine Learning Techniques.
Purpose: The current study aimed to compare traditional logistic regression models with machine learning algorithms to investigate the predictive ability of (a) communication performance at 3 years old on language outcomes at 10 years old and (b) broader developmental skills (motor, social, and adap...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 61; no. 8; pp. 1926 - 1945 |
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| Autores principales: | , , , , , , |
| Formato: | Artículo |
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American Speech-Language-Hearing Association
Aug2018
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=131143793&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 131143793 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: Aug2018 vid: 61 iid: 8 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 131143793 10.1044/2018_JSLHR-L-17-0210 ppf: 1926 ppct: 19 formats: fmt: @attributes: type: P size: 1.2MB tig: atl: Predicting Language Difficulties in Middle Childhood From Early Developmental Milestones: A Comparison of Traditional Regression and Machine Learning Techniques. aug: au: Armstrong, Rebecca Symons, Martyn Scott, James G. Arnott, Wendy L. Copland, David A. McMahon, Katie L. Whitehouse, Andrew J. O. affil: School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, Australia Centre for Clinical Research, University of Queensland, Brisbane, Australia Centre for Advanced Imaging, University of Queensland, Brisbane, Australia Telethon Kids Institute, University of Western Australia, Perth National Health and Medical Research Council (NHMRC) Fetal Alcohol Spectrum Disorder (FASD) Research Australia, Centre of Research Excellence, Perth Metro North Mental Health, Royal Brisbane and Women's Hospital, Australia Hear and Say, Brisbane, Australia School of Clinical Science, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia su: Algorithms Chi-squared test Child development deviations Computer software Confidence intervals Decision trees Language disorders in children Longitudinal method Multivariate analysis Questionnaires Logistic regression analysis Predictive validity Odds ratio sug: subj: Software publishers (except video game publishers) Computer and software stores Computer and Computer Peripheral Equipment and Software Merchant Wholesalers Computer, computer peripheral and pre-packaged software merchant wholesalers Algorithms Chi-squared test Child development deviations Computer software Confidence intervals Decision trees Language disorders in children Longitudinal method Multivariate analysis Questionnaires Logistic regression analysis Predictive validity Odds ratio ab: Purpose: The current study aimed to compare traditional logistic regression models with machine learning algorithms to investigate the predictive ability of (a) communication performance at 3 years old on language outcomes at 10 years old and (b) broader developmental skills (motor, social, and adaptive) at 3 years old on language outcomes at 10 years old. Method: Participants (N = 1,322) were drawn from the Western Australian Pregnancy Cohort (Raine) Study (Straker et al., 2017). A general developmental screener, the Infant Monitoring Questionnaire (Squires, Bricker, & Potter, 1990), was completed by caregivers at the 3-year follow-up. Language ability at 10 years old was assessed using the Clinical Evaluation of Language Fundamentals-Third Edition (Semel, Wiig, & Secord, 1995). Logistic regression models and interpretable machine learning algorithms were used to assess predictive abilities of early developmental milestones for later language outcomes. Results: Overall, the findings showed that prediction accuracies were comparable between logistic regression and machine learning models using communication-only performance as well as performance on communication and broader developmental domains to predict language performance at 10 years old. Decision trees are incorporated to visually present these findings but must be interpreted with caution because of the poor accuracy of the models overall. Conclusions: The current study provides preliminary evidence that machine learning algorithms provide equivalent predictive accuracy to traditional methods. Furthermore, the inclusion of broader developmental skills did not improve predictive capability. Assessment of language at more than 1 time point is necessary to ensure children whose language delays emerge later are identified and supported. Supplemental Material: https://doi.org/10.23641/asha. 6879719 pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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