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

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  • Nicole White
  • Helen Johnson
  • Peter Silburn
  • Kerrie Mengersen

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.

Suggested Citation

  • Nicole White & Helen Johnson & Peter Silburn & Kerrie Mengersen, 2012. "Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2363-2377, July.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2363-2377
    DOI: 10.1080/02664763.2012.710897
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    Cited by:

    1. Leonardo Grilli & Carla Rampichini, 2015. "Specification of random effects in multilevel models: a review," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 967-976, May.
    2. Patricia Gilholm & Kerrie Mengersen & Helen Thompson, 2020. "Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-17, June.

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