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Article

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

Translational psychiatry - 12 Aug 2014

Duda M, Kosmicki J A, Wall D P

Abstract excerpt

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...

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Identifiers

PMID: 25116834
DOI: 10.1038/tp.2014.65
PMCID: PMC4150240

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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