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28-06-2022 | Brief Report

Brief Report: Machine Learning for Estimating Prognosis of Children with Autism Receiving Early Behavioral Intervention—A Proof of Concept

Auteurs: Isabelle Préfontaine, Marc J. Lanovaz, Mélina Rivard

Gepubliceerd in: Journal of Autism and Developmental Disorders

Abstract

Although 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 necessary.

Because the intensity of the intervention provided to our sample may not qualify the intervention as being “intensive”, we will use the expression early behavioral intervention when referring to the program.

Special education technicians is a terminology unique to the province of Quebec and refers to college-level technicians.

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Titel: Brief Report: Machine Learning for Estimating Prognosis of Children with Autism Receiving Early Behavioral Intervention—A Proof of Concept
Auteurs: Isabelle Préfontaine
Marc J. Lanovaz
Mélina Rivard
Publicatiedatum: 28-06-2022
Uitgeverij: Springer US
Gepubliceerd in: Journal of Autism and Developmental Disorders
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI: https://doi.org/10.1007/s10803-022-05641-9

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