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dc.contributor.authorPréfontaine, Isabelle
dc.contributor.authorLanovaz, Marc
dc.contributor.authorRivard, Mélina
dc.subjectDifferential responsefr
dc.subjectEarly behavioral interventionfr
dc.subjectMachine learningfr
dc.titleBrief report : machine learning for estimating prognosis of children with autism receiving early behavioral intervention : a proof of conceptfr
dc.contributor.affiliationUniversité de Montréal. Faculté des arts et des sciences. École de psychoéducationfr
dcterms.abstractAlthough early behavioral intervention is considered as empirically-supported for children with autism, estimating treatment prognosis is a challenge for practitioners. One potential solution is to use machine learning to guide the prediction of the response to intervention. Thus, our study compared five machine algorithms in estimating treatment prognosis on two outcomes (i.e., adaptive functioning and autistic symptoms) in children with autism receiving early behavioral intervention in a community setting. Each machine learning algorithm produced better predictions than random sampling on both outcomes. Those results indicate that machine learning is a promising approach to estimating prognosis in children with autism, but studies comparing these predictions with those produced by qualified practitioners remain
UdeM.ReferenceFournieParDeposantPréfontaine, I., Lanovaz, M. J., & Rivard, M. (2022). Brief report: Machine learning for estimating prognosis of children with autism receiving early behavioral intervention – A proof of concept. Journal of Autism and Developmental Disorders.
UdeM.VersionRioxxVersion acceptée / Accepted Manuscriptfr
oaire.citationTitleJournal of autism and developmental disordersfr

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