Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study.

Gardner-Hoag, Julie; Novack, Marlena; Parlett-Pelleriti, Chelsea; Stevens, Elizabeth; Dixon, Dennis; Linstead, Erik

Gardner-Hoag, Julie; Novack, Marlena; Parlett-Pelleriti, Chelsea; Stevens, Elizabeth; Dixon, Dennis; Linstead, Erik.

Gardner-Hoag J; Schmid College of Science and Technology, Chapman University, Orange, CA, United States.
Novack M; Center for Autism and Related Disorders, Woodland Hills, CA, United States.
Parlett-Pelleriti C; Fowler School of Engineering, Chapman University, Orange, CA, United States.
Stevens E; Fowler School of Engineering, Chapman University, Orange, CA, United States.
Dixon D; Center for Autism and Related Disorders, Woodland Hills, CA, United States.
Linstead E; Fowler School of Engineering, Chapman University, Orange, CA, United States.

JMIR Med Inform ; 9(6): e27793, 2021 Jun 02.

Article en En | MEDLINE | ID: mdl-34076577

Palabras clave

autism; autism spectrum disorder; behavior; challenging behaviors; disorder; efficacy; engagement; impact; machine learning; retrospective; subtypes; treatment; treatment response; unsupervised machine learning

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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Año: 2021 Tipo del documento: Article

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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Año: 2021 Tipo del documento: Article