Prediction of Autism at 3 Years from Behavioural and Developmental Measures in High-Risk Infants: A Longitudinal Cross-Domain Classifier Analysis

Autor/inn/en	Bussu, G.; Jones, E. J. H.; Charman, T.; Johnson, M. H.; Buitelaar, J. K.; Baron-Cohen, S.; Bedford, R.; Bolton, P.; Blasi, A.; Chandler, S.; Cheung, C.; Davies, K.; Elsabbagh, M.; Fernandes, J.; Gammer, I.; Garwood, H.; Gliga, T.; Guiraud, J.; Hudry, K.; Liew, M.; Lloyd-Fox, S.; Maris, H.; O'Hara, L.; Pasco, G.; Pickles, A.; Ribeiro, H.; Salomone, E.; Tucker, L.; Volein, A.
Titel	Prediction of Autism at 3 Years from Behavioural and Developmental Measures in High-Risk Infants: A Longitudinal Cross-Domain Classifier Analysis
Quelle	In: Journal of Autism and Developmental Disorders, 48 (2018) 7, S.2418-2433 (16 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Bussu, G.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0162-3257
DOI	10.1007/s10803-018-3509-x
Schlagwörter	Prediction; Autism; Symptoms (Individual Disorders); Classification; At Risk Persons; Control Groups; Pervasive Developmental Disorders; Children; Motor Development; Young Children; Cognitive Ability; Behavior Rating Scales; Adjustment (to Environment); Infants; Toddlers; Longitudinal Studies; Child Development; Siblings; Questionnaires; Observation; Diagnostic Tests; Statistical Analysis; Mullen Scales of Early Learning; Vineland Adaptive Behavior Scales; Autism Diagnostic Observation Schedule + Suchen Sie Ihr Suchwort? Vorhersage; Autismus; Psychiatrische Symptomatik; Classification system; Klassifikation; Klassifikationssystem; Risikogruppe; Child; Kind; Kinder; Motorische Entwicklung; Frühe Kindheit; Denkfähigkeit; Infant; Toddler; Toddlers; Kleinkind; Infants; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Kindesentwicklung; Sibling; Geschwister; Fragebogen; Beobachtung; Diagnostic test; Diagnostischer Test; Statistische Analyse
Abstract	We integrated multiple behavioural and developmental measures from multiple time-points using machine learning to improve early prediction of individual Autism Spectrum Disorder (ASD) outcome. We examined Mullen Scales of Early Learning, Vineland Adaptive Behavior Scales, and early ASD symptoms between 8 and 36 months in high-risk siblings (HR; n = 161) and low-risk controls (LR; n = 71). Longitudinally, LR and "HR-Typical" showed higher developmental level and functioning, and fewer ASD symptoms than "HR-Atypical" and "HR-ASD." At 8 months, machine learning classified "HR-ASD" at chance level, and broader atypical development with 69.2% Area Under the Curve (AUC). At 14 months, ASD and broader atypical development were classified with approximately 71% AUC. Thus, prediction of ASD was only possible with moderate accuracy at 14 months. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01