• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of medical informatics >Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning
【24h】

Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning

机译：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

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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.
机译：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自闭症亚组，初步证据支持了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，该表型的大小足以稳健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。材料和方法：本研究包括ASD儿童（N = 2400），迄今为止最大的同类。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部分样品（n = 1034）。结果：通过高斯混合模型的应用揭示了16个亚组。通过分层聚类聚类进一步检查亚组，发现2种具有独特缺陷分布的重叠行为表型，每个缺陷表由这些缺陷严重程度不同的亚组组成。此外，在各亚型之间发现了对治疗的差异反应，归因于聚类的均质化效应，导致了较高的方差。讨论：回归模型解释的高方差量表明聚类为均质化提供了基础，因此有机会根据聚类成员资格来调整处理方式。这些发现对ASD的预后和靶向治疗具有重要意义，并为基于无监督机器学习的个性化干预铺平了道路。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号