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Toward Novel Tools for Autism Identification: Fusing Computational and Clinical Expertise / L. L. CORONA in Journal of Autism and Developmental Disorders, 51-11 (November 2021)
[article]
Titre : Toward Novel Tools for Autism Identification: Fusing Computational and Clinical Expertise Type de document : Texte imprimé et/ou numérique Auteurs : L. L. CORONA, Auteur ; L. WAGNER, Auteur ; J. WADE, Auteur ; Amy S. WEITLAUF, Auteur ; J. HINE, Auteur ; A. NICHOLSON, Auteur ; C. STONE, Auteur ; A. VEHORN, Auteur ; Zachary WARREN, Auteur Article en page(s) : p.4003-4012 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder/diagnosis Autistic Disorder Child Child, Preschool Humans Mass Screening Outcome Assessment, Health Care Assessment Autism spectrum disorder Machine learning Young children has served as a Consultant for Adaptive Technology Consulting and Roche. Dr. Weitlauf has served as a Consultant for Adaptive Technology Consulting. Index. décimale : PER Périodiques Résumé : 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. En ligne : http://dx.doi.org/10.1007/s10803-020-04857-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=454
in Journal of Autism and Developmental Disorders > 51-11 (November 2021) . - p.4003-4012[article] Toward Novel Tools for Autism Identification: Fusing Computational and Clinical Expertise [Texte imprimé et/ou numérique] / L. L. CORONA, Auteur ; L. WAGNER, Auteur ; J. WADE, Auteur ; Amy S. WEITLAUF, Auteur ; J. HINE, Auteur ; A. NICHOLSON, Auteur ; C. STONE, Auteur ; A. VEHORN, Auteur ; Zachary WARREN, Auteur . - p.4003-4012.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 51-11 (November 2021) . - p.4003-4012
Mots-clés : Autism Spectrum Disorder/diagnosis Autistic Disorder Child Child, Preschool Humans Mass Screening Outcome Assessment, Health Care Assessment Autism spectrum disorder Machine learning Young children has served as a Consultant for Adaptive Technology Consulting and Roche. Dr. Weitlauf has served as a Consultant for Adaptive Technology Consulting. Index. décimale : PER Périodiques Résumé : 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. En ligne : http://dx.doi.org/10.1007/s10803-020-04857-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=454