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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. – Journal of Autism and Developmental Disorders, 2018
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…
Descriptors: Prediction, Autism, Symptoms (Individual Disorders), Classification
van Eck, Mirjam; Dallmeijer, Annet J.; Voorman, Jeanine M.; Becher, Jules G. – Developmental Medicine & Child Neurology, 2009
Aim: The aim of this study was to describe the course of motor performance and analyse its relationship with motor capacity over a period of 3 years in 104 children (66 males, 38 females; 43% of those initially invited) with cerebral palsy (CP) aged 9, 11, and 13 years at the start of the study. Forty-one had hemiplegia, 42 diplegia, 21…
Descriptors: Cerebral Palsy, Classification, Motor Development, Children