Brief Report: Machine Learning for Estimating Prognosis of Children with Autism Receiving Early Behavioral Intervention—A Proof of Concept.
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...
| Publicado en: | Journal of Autism & Developmental Disorders Vol. 54; no. 4; pp. 1605 - 1611 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177351459&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177351459 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01623257 AUT jtl: Journal of Autism & Developmental Disorders issn: 01623257 maglogo: N pubinfo: dt: Apr2024 vid: 54 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177351459 157682500 177351459 177351459 10.1007/s10803-022-05641-9 177351459 ppf: 1605 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Brief Report: Machine Learning for Estimating Prognosis of Children with Autism Receiving Early Behavioral Intervention—A Proof of Concept. aug: au: Préfontaine, Isabelle Lanovaz, Marc J. Rivard, Mélina affil: https://ror.org/0161xgx34 École de psychoéducation, Université de Montréal, Montréal, QC, Canada sug: subj: Machine Learning Children with Disabilities Autism Spectrum Disorder Prognosis Early Intervention Behavior and Behavior Mechanisms Treatment Outcomes Human Funding Source Pilot Studies Adaptation, Psychological ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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