Cited by 9Bayesian Methods and Mixture ModelsAdvanced Clustering Algorithms ResearchBayesian Modeling and Causal Inference

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

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White, Nicole, Johnson, Helen, Silburn, Peter, Mengersen, Kerrie·2012·Journal of Applied Statistics

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.

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  1. J.A. Hartigan and M.A. Wong, A k-means clustering algorithm, Journal of the Royal Statistical Society Series C 28 (1979), pp. 100-108.
  2. K.S. Kendler, L.M. Karkowski and D. Walsh, The structure of psy- chosis: latent class analysis of probands from the Roscommon Family Study, Archives of General Psychiatry 55 (1998), pp. 492-509.
  3. G. Schwartz, Estimating the dimension of a model, Annals of Statistics 6 (1978), pp. 461-464.
  4. M. West, P. Müller and MD. Escobar, Hierarchical Priors and Mixture Models, with Application in Regression and Density Estimation, In: Aspects of Uncertainty: A Tribute to D. V. Lindley (eds. AFM. Smith and P. Free- man), Wiley. 1994. pp. 363-386.

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Authors
White, N., Johnson, H., Silburn, P., & Mengersen, K.
Affiliations
Queensland University of Technology (AU)
Corresponding author
White, Nicole
Venue
Journal of Applied Statistics
Published
2012-11
Volume
39
Issue
11
Pages
2363-2377
ISSN
  • 0266-4763 (ISSN-L)
  • 1360-0532
OpenAlex
W2087566150
Cited by
9
References
60
Citations by year
2024: 2 · 2021: 3 · 2020: 1 · 2018: 1 · 2016: 1 · 2014: 1
FWCI
0.31
Type
journal article
Language
en
Open access
Locations
  • Journal of Applied Statistics (publishedVersion)
  • QUT ePrints (Queensland University of Technology) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Keywords
Dirichlet process mixture model, unsupervised clustering, phenotypic identification, Parkinson's disease, symptom profiles, Unified Parkinson's Disease Rating Scale, disease subgroups

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