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Transl Psychiatry DOI:10.1038/tp.2014.65

Testing the accuracy of an observation-based classifier for rapid detection of autism risk.

Publication TypeJournal Article
Year of Publication2014
AuthorsDuda, M, Kosmicki, JA, Wall, DP
JournalTransl Psychiatry
Volume4
Pagese424
Date Published2014 Aug 12
ISSN2158-3188
KeywordsAlgorithms, Artificial Intelligence, Child, Child Development Disorders, Pervasive, Child, Preschool, Diagnosis, Computer-Assisted, Female, Genetic Heterogeneity, Humans, Male, Personality Assessment, Phenotype, Pilot Projects, Psychometrics, Reproducibility of Results, Software Design
Abstract

Current 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.

URLhttp://dx.doi.org/10.1038/tp.2014.65
DOI10.1038/tp.2014.65
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/25116834?dopt=Abstract

Alternate JournalTransl Psychiatry
PubMed ID25116834
PubMed Central IDPMC4150240
Grant ListR01 MH090611 / MH / NIMH NIH HHS / United States
1R01MH090611-01A1 / MH / NIMH NIH HHS / United States