title: Testing the accuracy of an observation-based classifier for rapid detection of autism risk.
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.
authors.affiliation: Division of Systems Medicine, Department of Pediatrics, Stanford University, Stanford, CA, USA.
authors.forename: M
authors.initials: M
authors.lastname: Duda
authors.affiliation: 1] Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA [2] Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
authors.forename: J A
authors.initials: JA
authors.lastname: Kosmicki
authors.affiliation: Division of Systems Medicine, Department of Pediatrics, Stanford University, Stanford, CA, USA.
authors.forename: D P
authors.initials: DP
authors.lastname: Wall
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002648:Child; D002659:Child Development Disorders, Pervasive / Q000145:classification* / Q000175:diagnosis* / Q000235:genetics; D002675:Child, Preschool; D003936:Diagnosis, Computer-Assisted; D005260:Female; D018740:Genetic Heterogeneity; D006801:Humans; D008297:Male; D010552:Personality Assessment / Q000706:statistics & numerical data; D010641:Phenotype; D010865:Pilot Projects; D011594:Psychometrics / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D012985:Software Design