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dc.contributor.authorDuda, Men_US
dc.contributor.authorKosmicki, J Aen_US
dc.contributor.authorWall, D Pen_US
dc.date.accessioned2014-10-01T14:29:26Z
dc.date.issued2014en_US
dc.identifier.citationDuda, M, J A Kosmicki, and D P Wall. 2014. “Testing the accuracy of an observation-based classifier for rapid detection of autism risk.” Translational Psychiatry 4 (8): e424. doi:10.1038/tp.2014.65. http://dx.doi.org/10.1038/tp.2014.65.en
dc.identifier.issn2158-3188en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12987396
dc.description.abstractCurrent approaches for diagnosing autism have high diagnostic validity but are time consuming and can contribute to delays in arriving at an official diagnosis. In a pilot study, we used machine learning to derive a classifier that represented a 72% reduction in length from the gold-standard Autism Diagnostic Observation Schedule-Generic (ADOS-G), while retaining >97% statistical accuracy. The pilot study focused on a relatively small sample of children with and without autism. The present study sought to further test the accuracy of the classifier (termed the observation-based classifier (OBC)) on an independent sample of 2616 children scored using ADOS from five data repositories and including both spectrum (n=2333) and non-spectrum (n=283) individuals. We tested OBC outcomes against the outcomes provided by the original and current ADOS algorithms, the best estimate clinical diagnosis, and the comparison score severity metric associated with ADOS-2. The OBC was significantly correlated with the ADOS-G (r=−0.814) and ADOS-2 (r=−0.779) and exhibited >97% sensitivity and >77% specificity in comparison to both ADOS algorithm scores. The correspondence to the best estimate clinical diagnosis was also high (accuracy=96.8%), with sensitivity of 97.1% and specificity of 83.3%. The correlation between the OBC score and the comparison score was significant (r=−0.628), suggesting that the OBC provides both a classification as well as a measure of severity of the phenotype. These results further demonstrate the accuracy of the OBC and suggest that reductions in the process of detecting and monitoring autism are possible.en
dc.language.isoen_USen
dc.publisherNature Publishing Groupen
dc.relation.isversionofdoi:10.1038/tp.2014.65en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4150240/pdf/en
dash.licenseLAAen_US
dc.titleTesting the accuracy of an observation-based classifier for rapid detection of autism risken
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalTranslational Psychiatryen
dc.date.available2014-10-01T14:29:26Z
dc.identifier.doi10.1038/tp.2014.65*


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