Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder.

ABSTRACT: The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts population-based surveillance of autism spectrum disorder (ASD) among 8-year old children in multiple US sites. To classify ASD, trained clinicians review developmental evaluations collected from multiple health and education sources to determine whether the child meets the ASD surveillance case criteria. The number of evaluations collected has dramatically increased since the year 2000, challenging the resources and timeliness of the surveillance system. We developed and evaluated a machine learning approach to classify case status in ADDM using words and phrases contained in children's developmental evaluations. We trained a random forest classifier using data from the 2008 Georgia ADDM site which included 1,162

SUBMITTER: Maenner MJ 

PROVIDER: S-EPMC5176307 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature