Use of Machine Learning to Shorten Observation-based Screening and Diagnosis of Autism

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Use of Machine Learning to Shorten Observation-based Screening and Diagnosis of Autism

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dc.contributor.author Kosmicki, Jack Alphonse
dc.contributor.author DeLuca, Todd
dc.contributor.author Harstad, Elizabeth Borges
dc.contributor.author Fusaro, Vincent Alfred
dc.contributor.author Wall, Dennis Paul
dc.date.accessioned 2012-12-07T19:23:48Z
dc.date.issued 2012
dc.identifier.citation Wall, D.P., J. Kosmicki, T.F. DeLuca, E. Harstad, and V.A. Fusaro. 2012. Use of machine learning to shorten observation-based screening and diagnosis of autism. Translational Psychiatry 2(4): e100. en_US
dc.identifier.issn 2158-3188 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:10018943
dc.description.abstract The Autism Diagnostic Observation Schedule-Generic (ADOS) is one of the most widely used instruments for behavioral evaluation of autism spectrum disorders. It is composed of four modules, each tailored for a specific group of individuals based on their language and developmental level. On average, a module takes between 30 and 60 min to deliver. We used a series of machine-learning algorithms to study the complete set of scores from Module 1 of the ADOS available at the Autism Genetic Resource Exchange (AGRE) for 612 individuals with a classification of autism and 15 non-spectrum individuals from both AGRE and the Boston Autism Consortium (AC). Our analysis indicated that 8 of the 29 items contained in Module 1 of the ADOS were sufficient to classify autism with 100% accuracy. We further validated the accuracy of this eight-item classifier against complete sets of scores from two independent sources, a collection of 110 individuals with autism from AC and a collection of 336 individuals with autism from the Simons Foundation. In both cases, our classifier performed with nearly 100% sensitivity, correctly classifying all but two of the individuals from these two resources with a diagnosis of autism, and with 94% specificity on a collection of observed and simulated non-spectrum controls. The classifier contained several elements found in the ADOS algorithm, demonstrating high test validity, and also resulted in a quantitative score that measures classification confidence and extremeness of the phenotype. With incidence rates rising, the ability to classify autism effectively and quickly requires careful design of assessment and diagnostic tools. Given the brevity, accuracy and quantitative nature of the classifier, results from this study may prove valuable in the development of mobile tools for preliminary evaluation and clinical prioritization—in particular those focused on assessment of short home videos of children—that speed the pace of initial evaluation and broaden the reach to a significantly larger percentage of the population at risk. en_US
dc.language.iso en_US en_US
dc.publisher Nature Publishing Group en_US
dc.relation.isversionof doi:10.1038/tp.2012.10 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3337074/pdf/ en_US
dash.license LAA
dc.subject autism classification en_US
dc.subject autism classifier en_US
dc.subject algorithm to classify autism en_US
dc.subject autism diagnostic observation schedule en_US
dc.subject autism spectrum disorders en_US
dc.subject machine learning en_US
dc.subject rapid detection of autism en_US
dc.title Use of Machine Learning to Shorten Observation-based Screening and Diagnosis of Autism en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal Translational Psychiatry en_US
dash.depositing.author Wall, Dennis Paul
dc.date.available 2012-12-07T19:23:48Z

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