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.

中文翻译：

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通过无监督机器学习识别和分析自闭症谱系障碍的行为表型。

背景与目的自闭症谱系障碍（ASD）是一种异质性障碍。研究已经探索了潜在的ASD亚组，并提供了初步的证据来支持行为和遗传上不同的亚组的存在。然而，研究还没有利用机器学习来识别表型，其规模足够大，可以有效地检查此类亚组的治疗反应。本研究的目的是应用高斯混合模型和层次聚类来识别ASD的行为表型，并检查所学表型的治疗反应。材料与方法本研究包括ASD（N = 2400）儿童的样本，这是迄今为止最大的样本。无监督机器学习已应用于模型ASD子组及其分类关系。回顾性治疗数据可用于一部分样品（n = 1034）。通过回归在每个亚组中检查治疗反应。结果高斯混合模型的应用揭示了16个亚组。通过分层聚集聚类对亚组进行进一步检查，发现了2种具有独特缺陷特征的重叠行为表型，每个缺陷型由这些缺陷严重程度不同的亚组组成。此外，在亚型之间发现了对治疗的差异化反应，由于聚类的均质化作用，导致了明显更高的差异。讨论回归模型解释的大量方差表明，聚类为均化提供了基础，因此有机会根据聚类成员资格来定制处理。