[article] 
inJournal of Autism and Developmental Disorders > 48-7 (July 2018) . - p.2418-2433

Titre :	Prediction of Autism at 3 Years from Behavioural and Developmental Measures in High-Risk Infants: A Longitudinal Cross-Domain Classifier Analysis
Type de document :	Texte imprimé et/ou numérique
Auteurs :	G. BUSSU, Auteur ; E. J. H. JONES, Auteur ; Tony CHARMAN, Auteur ; M. H. JOHNSON, Auteur ; Jan K. BUITELAAR, Auteur
Article en page(s) :	p.2418-2433
Langues :	Anglais (eng)
Mots-clés :	Autism  Data integration  Early prediction  High-risk  Individual prediction  Longitudinal study  Machine learning
Index. décimale :	PER Périodiques
Résumé :	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.
En ligne :	http://dx.doi.org/10.1007/s10803-018-3509-x
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=367

[article] Prediction of Autism at 3 Years from Behavioural and Developmental Measures in High-Risk Infants: A Longitudinal Cross-Domain Classifier Analysis [Texte imprimé et/ou numérique] / G. BUSSU, Auteur ; E. J. H. JONES, Auteur ; Tony CHARMAN, Auteur ; M. H. JOHNSON, Auteur ; Jan K. BUITELAAR, Auteur . - p.2418-2433.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 48-7 (July 2018) . - p.2418-2433

Mots-clés :	Autism  Data integration  Early prediction  High-risk  Individual prediction  Longitudinal study  Machine learning
Index. décimale :	PER Périodiques
Résumé :	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.
En ligne :	http://dx.doi.org/10.1007/s10803-018-3509-x
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=367