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

White, Nicole, Johnson, Helen, & Silburn, Peter A. (2012) Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease. Journal of Applied Statistics.

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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 (DPM) model for this task. This model is defined by the placement of the Dirichlet Process (DP) 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 (PD) is considered, with symptom profiles collected using the Unified Parkinson’s Disease Rating Scale (UPDRS). Clustering, Dirichlet Process mixture, Parkinson’s disease, UPDRS.

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2 citations in Scopus
2 citations in Web of Science®
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ID Code: 53292
Item Type: Journal Article
Refereed: Yes
Additional Information: Version of record first published: 01 Aug 2012
Keywords: clustering , Dirichlet process mixture, Parkinson's disease, UPDRS
DOI: 10.1080/02664763.2012.710897
ISSN: 0266-4763
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Taylor & Francis
Deposited On: 23 Aug 2012 22:06
Last Modified: 04 Sep 2013 09:35

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