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Journal of Autism and Developmental Disorders

Gepubliceerd in:

08-01-2021 | Original Paper

Toward Novel Tools for Autism Identification: Fusing Computational and Clinical Expertise

Auteurs: Laura L. Corona, Liliana Wagner, Joshua Wade, Amy S. Weitlauf, Jeffrey Hine, Amy Nicholson, Caitlin Stone, Alison Vehorn, Zachary Warren

Gepubliceerd in: Journal of Autism and Developmental Disorders | Uitgave 11/2021

Abstract

Barriers to identifying autism spectrum disorder (ASD) in young children in a timely manner have led to calls for novel screening and assessment strategies. Combining computational methods with clinical expertise presents an opportunity for identifying patterns within large clinical datasets that can inform new assessment paradigms. The present study describes an analytic approach used to identify key features predictive of ASD in young children, drawn from large amounts of data from comprehensive diagnostic evaluations. A team of expert clinicians used these predictive features to design a set of assessment activities allowing for observation of these core behaviors. The resulting brief assessment underlies several novel approaches to the identification of ASD that are the focus of ongoing research.

vorige artikel Dimensions of Autistic Traits Rated by Parents of Children and Adolescents with Suspected Autism Spectrum Disorders

volgende artikel Evidence-Based Practices for Children, Youth, and Young Adults with Autism: Third Generation Review

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Titel: Toward Novel Tools for Autism Identification: Fusing Computational and Clinical Expertise
Auteurs: Laura L. Corona
Liliana Wagner
Joshua Wade
Amy S. Weitlauf
Jeffrey Hine
Amy Nicholson
Caitlin Stone
Alison Vehorn
Zachary Warren
Publicatiedatum: 08-01-2021
Uitgeverij: Springer US
Gepubliceerd in: Journal of Autism and Developmental Disorders / Uitgave 11/2021
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI: https://doi.org/10.1007/s10803-020-04857-x

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