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Identification And Analysis Of Behavioral Phenotypes In Autism Spectrum Disorder Via Unsupervised Machine Learning, Elizabeth Stevens, Dennis R. Dixon, Marlena N. Novack, Doreen Granpeesheh, Tristram Smith, Erik Linstead

Engineering Faculty Articles and Research

Background and objective: Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary evidence supporting the existence of behaviorally and genetically distinct subgroups; however, research has yet to leverage machine learning to identify phenotypes on a scale large enough to robustly examine treatment response across such subgroups. The purpose of the present study was to apply Gaussian Mixture Models and Hierarchical Clustering to identify behavioral phenotypes of ASD and examine treatment response across the learned phenotypes.

Materials and methods: The present study included a sample of children with ASD (N = 2400), …