Literaturnachweis - Detailanzeige
| Autor/inn/en | Corona, Laura L.; Wagner, Liliana; Wade, Joshua; Weitlauf, Amy S.; Hine, Jeffrey; Nicholson, Amy; Stone, Caitlin; Vehorn, Alison; Warren, Zachary |
|---|---|
| Titel | Toward Novel Tools for Autism Identification: Fusing Computational and Clinical Expertise |
| Quelle | In: Journal of Autism and Developmental Disorders, 51 (2021) 11, S.4003-4012 (10 Seiten)
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| Zusatzinformation | ORCID (Corona, Laura L.) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 0162-3257 |
| DOI | 10.1007/s10803-020-04857-x |
| Schlagwörter | Autism; Pervasive Developmental Disorders; Identification; Clinical Diagnosis; Expertise; Young Children; Predictor Variables; Diagnostic Tests; Observation |
| 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. (As Provided). |
| Anmerkungen | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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