Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease

Nicole White; Helen Johnson; Peter Silburn; Kerrie Mengersen

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Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease

机译：Dirichlet过程混合模型用于帕金森氏病症状的无监督聚类

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In this paper, the goal of identifying disease subgroups based on differences in observed symptom profile is considered. Commonly referred to as phenotype identification, solutions to this task often involve the application of unsupervised clustering techniques. In this paper, we investigate the application of a Dirichlet process mixture model for this task. This model is defined by the placement of the Dirichlet process on the unknown components of a mixture model, allowing for the expression of uncertainty about the partitioning of observed data into homogeneous subgroups. To exemplify this approach, an application to phenotype identification in Parkinson's disease is considered, with symptom profiles collected using the Unified Parkinson's Disease Rating Scale.

机译：在本文中，考虑了基于观察到的症状特征差异来识别疾病亚组的目标。通常称为表型识别，此任务的解决方案通常涉及无监督聚类技术的应用。在本文中，我们研究了Dirichlet过程混合模型在此任务中的应用。该模型是通过将Dirichlet过程放置在混合模型的未知成分上来定义的，从而允许表达有关将观察到的数据划分为同质子组的不确定性。为了举例说明这种方法，考虑使用帕金森病统一病情量表收集的症状特征，将其应用于帕金森氏病的表型鉴定。

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来源
《Journal of applied statistics》 |2012年第12期|2363-2377|共15页
作者
Nicole White; Helen Johnson; Peter Silburn; Kerrie Mengersen;
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作者单位

Mathematical Sciences, Queensland University of Technology, Brisbane, Australia;

Mathematical Sciences, Queensland University of Technology, Brisbane, Australia;

St. Andrew's War Memorial Hospital and Medical Institute, Brisbane, Australia;

Mathematical Sciences, Queensland University of Technology, Brisbane, Australia;

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正文语种 eng
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关键词
clustering; dirichlet process mixture; parkinson's disease; UPDRS;

机译：集群Dirichlet工艺混合物帕金森氏病UPDRS;

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7. Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease [O] . White Nicole, Johnson Helen, Silburn Peter A. 2012

机译：Dirichlet过程混合模型用于帕金森氏病症状的无监督聚类

Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease

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