Predicting Language Difficulties in Middle Childhood from Early Developmental Milestones: A Comparison of Traditional Regression and Machine Learning Techniques

Autor/inn/en	Armstrong, Rebecca; Symons, Martyn; Scott, James G.; Arnott, Wendy L.; Copland, David A.; McMahon, Katie L.; Whitehouse, Andrew J. O.
Titel	Predicting Language Difficulties in Middle Childhood from Early Developmental Milestones: A Comparison of Traditional Regression and Machine Learning Techniques
Quelle	In: Journal of Speech, Language, and Hearing Research, 61 (2018) 8, S.1926-1944 (19 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1092-4388
DOI	10.1044/2018_JSLHR-L-17-0210
Schlagwörter	Foreign Countries; Regression (Statistics); Language Tests; Predictive Validity; Screening Tests; Toddlers; Children; Longitudinal Studies; Language Skills; Language Acquisition; Developmental Stages; Communication Skills; Accuracy; Comparative Analysis; Delayed Speech; Language Impairments; Computational Linguistics; Australia; Clinical Evaluation of Language Fundamentals + Suchen Sie Ihr Suchwort? Ausland; Regression; Regressionsanalyse; Language test; Sprachtest; Screening-Verfahren; Infant; Infants; Toddler; Kleinkind; Child; Kind; Kinder; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Language skill; Sprachkompetenz; Sprachaneignung; Spracherwerb; Kommunikationsstil; Sprachverzögerung; Speech disorder; Speech disorders; Speech disabilities; Speech disability; Speech handicap; Speech handicaps; Speech impairment; Speech impairments; Language handicaps; Sprachbehinderung; Linguistics; Computerlinguistik; Australien
Abstract	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. (As Provided).
Anmerkungen	American Speech-Language-Hearing Association. 2200 Research Blvd #250, Rockville, MD 20850. Tel: 301-296-5700; Fax: 301-296-8580; e-mail: slhr@asha.org; Web site: http://jslhr.pubs.asha.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01