[article] 
inJournal of Autism and Developmental Disorders > 51-11 (November 2021) . - p.4003-4012

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

[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