عنوان انگلیسی مقاله:
Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning
ترجمه فارسی عنوان مقاله:
شناسایی و تجزیه و تحلیل فنوتیپ های رفتاری در اختلال طیف اوتیسم از طریق یادگیری ماشین بدون نظارت
Sciencedirect - Elsevier - International Journal of Medical Informatics, 129 (2019) 29-36: doi:10:1016/j:ijmedinf:2019:05:006
Elizabeth Stevensa, Dennis R. Dixonb, Marlena N. Novackb, Doreen Granpeeshehb, Tristram Smithc, Erik Linsteada
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), the largest of its
kind to date. Unsupervised machine learning was applied to model ASD subgroups as well as their taxonomic
relationships. Retrospective treatment data were available for a portion of the sample (n =1034). Treatment
response was examined within each subgroup via regression.
Results: The application of a Gaussian Mixture Model revealed 16 subgroups. Further examination of the subgroups
through Hierarchical Agglomerative Clustering suggested 2 overlying behavioral phenotypes with unique
deficit profiles each composed of subgroups that differed in severity of those deficits. Furthermore, differentiated
response to treatment was found across subtypes, with a substantially higher amount of variance accounted for
due to the homogenization effect of the clustering.
Discussion: The high amount of variance explained by the regression models indicates that clustering provides a
basis for homogenization, and thus an opportunity to tailor treatment based on cluster memberships. These
findings have significant implications on prognosis and targeted treatment of ASD, and pave the way for personalized
intervention based on unsupervised machine learning.
Keywords: Machine learning | Autism spectrum disorder | Behavioral phenotypes | Cluster analysis | Treatment response