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

Nicole White, Helen Johnson, Peter Silburn and Kerrie Mengersen

Journal of Applied Statistics, 2012, vol. 39, issue 11, 2363-2377

Abstract: 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.

Date: 2012
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Citations: View citations in EconPapers (2)

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DOI: 10.1080/02664763.2012.710897

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