Toward Novel Tools for Autism Identification: Fusing Computational and Clinical Expertise.
Assessment
Autism spectrum disorder
Machine learning
Young children
Journal
Journal of autism and developmental disorders
ISSN: 1573-3432
Titre abrégé: J Autism Dev Disord
Pays: United States
ID NLM: 7904301
Informations de publication
Date de publication:
Nov 2021
Nov 2021
Historique:
accepted:
19
12
2020
pubmed:
9
1
2021
medline:
15
10
2021
entrez:
8
1
2021
Statut:
ppublish
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.
Identifiants
pubmed: 33417138
doi: 10.1007/s10803-020-04857-x
pii: 10.1007/s10803-020-04857-x
pmc: PMC7791904
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4003-4012Subventions
Organisme : National Institutes of Health (US)
ID : R44MH115528
Organisme : NIMH NIH HHS
ID : R21 MH118539
Pays : United States
Organisme : NIMH NIH HHS
ID : R44 MH115528
Pays : United States
Organisme : National Institutes of Health (US)
ID : R43MH115528
Organisme : NIH/NIMH
ID : R21MH118539
Organisme : Eunice Kennedy Shriver National Institute of Child Health and Human Development
ID : U54 HD08321
Organisme : Center for Clinical and Translational Sciences, University of Texas Health Science Center at Houston (US)
ID : 5UL1TR002243-03
Organisme : NICHD NIH HHS
ID : P50 HD103537
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002243
Pays : United States
Organisme : NIMH NIH HHS
ID : R43 MH115528
Pays : United States
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.
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