Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease
Open accessAbstract
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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Dirichlet process mixture models in PD subtyping discover both the number of subtypes and the representative clinical characteristics of each subtype.
“For example, Dirichlet process mixture models in PD subtyping discover both the number of subtypes and the representative clinical characteristics of each subtype. 21”
From Machine learning for large‐scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures (2016) · cites this paper as [21]
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Cites
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) · doi:10.1201/b16018 · Cited by 27,276 · see citing works
Algorithm AS 136: A K-Means Clustering Algorithm
Hartigan, J. A., & Wong, M. A. (1979) · Applied Statistics · doi:10.2307/2346830 · Cited by 14,523 · see citing works
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999) · ACM Computing Surveys · doi:10.1145/331499.331504 · Cited by 13,532 · see citing works
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991 · see citing works
Hierarchical Clustering Schemes
Johnson, S. C. (1967) · Psychometrika · doi:10.1007/bf02289588 · Cited by 4,948 · see citing works
A Bayesian Analysis of Some Nonparametric Problems
Ferguson, T. S. (1973) · The Annals of Statistics · doi:10.1214/aos/1176342360 · Cited by 4,814 · see citing works
Variable Selection via Gibbs Sampling
George, E. I., & McCulloch, R. E. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476353 · Cited by 2,828 · see citing works
Bayesian Density Estimation and Inference Using Mixtures
Escobar, M. D., & West, M. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476550 · Cited by 2,237 · see citing works
Markov Chain Sampling Methods for Dirichlet Process Mixture Models
Neal, R. M. (2000) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2000.10474879 · Cited by 2,204 · see citing works
Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems
Antoniak, C. E. (1974) · The Annals of Statistics · doi:10.1214/aos/1176342871 · Cited by 1,976 · see citing works
The Unified Parkinson's Disease Rating Scale (UPDRS): Status and recommendations
Movement Disorder Society Task Force on Rating Scales for Parkinson's Disease (2003) · Movement Disorders · doi:10.1002/mds.10473 · Cited by 1,811 · see citing works
Variational inference for Dirichlet process mixtures
Jordan, M. I., & Jordan, M. I. (2006) · Bayesian Analysis · doi:10.1214/06-ba104 · Cited by 1,645 · see citing works
A Constructive Definition of Dirichlet Priors
Sethuraman, J. (1991) · doi:10.21236/ada238689 · Cited by 1,605 · see citing works
Finite Mixture and Markov Switching Models
Frühwirth‐Schnatter, S. (2006) · Springer eBooks · doi:10.1007/978-0-387-35768-3 · Cited by 1,584 · see citing works
Gibbs Sampling Methods for Stick-Breaking Priors
Ishwaran, H., & James, L. F. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501750332758 · Cited by 1,553 · see citing works
Neal, R. M. (2003) · The Annals of Statistics · doi:10.1214/aos/1056562461 · Cited by 1,380 · see citing works
Ferguson Distributions Via Polya Urn Schemes
Blackwell, D., & MacQueen, J. B. (1973) · The Annals of Statistics · doi:10.1214/aos/1176342372 · Cited by 1,343 · see citing works
Slice SamplingPreprint
Neal, R. M. (2000) · arXiv (Cornell University) · Cited by 1,151 · see citing works
Dealing With Label Switching in Mixture Models
Stephens, M. (2000) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00265 · Cited by 1,104 · see citing works
Epidemiology of parkinson’s disease
Tanner, C. M., & Goldman, S. M. (1996) · Neurologic Clinics · doi:10.1016/s0733-8619(05)70259-0 · Cited by 640 · see citing works
Computational and Inferential Difficulties with Mixture Posterior Distributions
Celeux, G., Hurn, M. A., & Robert, C. P. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474285 · Cited by 581 · see citing works
Estimating Normal Means with a Dirichlet Process Prior
Escobar, M. D. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476468 · Cited by 565 · see citing works
Heterogeneity of Parkinson's disease in the early clinical stages using a data driven approach
Lewis, S. J. G. (2005) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.2003.033530 · Cited by 547 · see citing works
Estimating Mixture of Dirichlet Process Models
MacEachern, S. N., & Müller, P. (1998) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1998.10474772 · Cited by 543 · see citing works
A Split-Merge Markov chain Monte Carlo Procedure for the Dirichlet Process Mixture Model
Jain, S., & Neal, R. M. (2004) · Journal of Computational and Graphical Statistics · doi:10.1198/1061860043001 · Cited by 459 · see citing works
Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models
Frühwirth‐Schnatter, S. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501750333063 · Cited by 454 · see citing works
Sampling the Dirichlet Mixture Model with Slices
Walker, S. G. (2007) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610910601096262 · Cited by 437 · see citing works
Estimating normal means with a conjugate style dirichlet process prior
MacEachern, S. N. (1994) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610919408813196 · Cited by 415 · see citing works
Bayesian Modelling and Inference on Mixtures of Distributions
Marin, J., Mengersen, K. L., & Robert, C. P. (2005) · Handbook of statistics · doi:10.1016/s0169-7161(05)25016-2 · Cited by 378 · see citing works
Self-citationKalli, M., Griffin, J. E., & Walker, S. G. (2009) · Statistics and Computing · doi:10.1007/s11222-009-9150-y · Cited by 344 · see citing works
Binder, D. A. (1978) · Biometrika · doi:10.1093/biomet/65.1.31 · Cited by 300 · see citing works
Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model
Dahl, D. B. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511584589.011 · Cited by 294 · see citing works
Bayesian infinite mixture model based clustering of gene expression profiles
Medvedovic, M., & Sivaganesan, S. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.9.1194 · Cited by 285 · see citing works
Markov chain Monte Carlo in approximate Dirichlet and beta two-parameter process hierarchical models
Ishwaran, H. (2000) · Biometrika · doi:10.1093/biomet/87.2.371 · Cited by 284 · see citing works
Modelling Heterogeneity With and Without the Dirichlet Process
Green, P. J., & Richardson, S. T. (2001) · Scandinavian Journal of Statistics · doi:10.1111/1467-9469.00242 · Cited by 280 · see citing works
The heterogeneity of idiopathic Parkinson's disease
Foltynie, T., Brayne, C., & Barker, R. A. (2002) · Journal of Neurology · doi:10.1007/pl00007856 · Cited by 255 · see citing works
Kendler, K. S., Karkowski, L. M., & Walsh, D. (1998) · Archives of General Psychiatry · doi:10.1001/archpsyc.55.6.492 · Cited by 245 · see citing works
The identification of Parkinson's disease subtypes using cluster analysis: A systematic review
van Rooden, S. M., Heiser, W. J., Kok, J. N., Verbaan, D., van Hilten, J. J., & Marinus, J. (2010) · Movement Disorders · doi:10.1002/mds.23116 · Cited by 238 · see citing works
Approximate Dirichlet Process Computing in Finite Normal Mixtures
Ishwaran, H., & James, L. F. (2002) · Journal of Computational and Graphical Statistics · doi:10.1198/106186002411 · Cited by 229 · see citing works
Clinical subtypes of Parkinson's disease
van Rooden, S. M., Colas, F., Martínez‐Martín, P., Jeukens‐Visser, M., Verbaan, D., Marinus, J., Chaudhuri, К. R., Kok, J. N., & van Hilten, J. J. (2010) · Movement Disorders · doi:10.1002/mds.23346 · Cited by 228 · see citing works
Bayesian Model-Based Clustering Procedures
Lau, J. W., & Green, P. J. (2007) · Journal of Computational and Graphical Statistics · doi:10.1198/106186007x238855 · Cited by 224 · see citing works
Controlling the Reinforcement in Bayesian Non-Parametric Mixture Models
Lijoi, A., Mena, R. H., & Prünster, I. (2007) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00609.x · Cited by 212 · see citing works
The association between motor subtypes and psychopathology in Parkinson's disease
Reijnders, J. S. A. M., Ehrt, U., Lousberg, R. L., Aarsland, D., & Leentjens, A. F. G. (2008) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2008.09.003 · Cited by 210 · see citing works
Models beyond the Dirichlet process
Lijoi, A., & Prünster, I. (2010) · Cambridge University Press eBooks · doi:10.1017/cbo9780511802478.004 · Cited by 202 · see citing works
Distinguishing phenotypes of childhood wheeze and cough using latent class analysis
Spycher, B. D., Silverman, M. A., Brooke, A., Minder, C. E., & Kuehni, C. E. (2008) · European Respiratory Journal · doi:10.1183/09031936.00153507 · Cited by 189 · see citing works
Parkinson's disease subtypes: clinical classification and ventricular cerebrospinal fluid analysis
Schiess, M. C., Zheng, H., Soukup, V. M., Bonnen, J. G., & Nauta, H. J. W. (2000) · Parkinsonism & Related Disorders · doi:10.1016/s1353-8020(99)00051-6 · Cited by 175 · see citing works
Variable selection in clustering via Dirichlet process mixture models
Kim, S., Tadesse, M. G., & Vannucci, M. (2006) · Biometrika · doi:10.1093/biomet/93.4.877 · Cited by 171 · see citing works
A Kth Nearest Neighbour Clustering Procedure
Wong, M. A., & Lane, T. (1983) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1983.tb01262.x · Cited by 138 · see citing works
Discreteness of Ferguson Selections
Blackwell, D. (1973) · The Annals of Statistics · doi:10.1214/aos/1176342373 · Cited by 136 · see citing works
Splitting and merging components of a nonconjugate Dirichlet process mixture model
Jain, S., & Neal, R. M. (2007) · Bayesian Analysis · doi:10.1214/07-ba219 · Cited by 132 · see citing works
Nyholt, D. R., Gillespie, N. A., Heath, A. C., Merikangas, K. R., Duffy, D. L., & Martin, N. G. (2004) · Genetic Epidemiology · doi:10.1002/gepi.10311 · Cited by 131 · see citing works
Improved criteria for clustering based on the posterior similarity matrix
Fritsch, A., & Ickstadt, K. (2009) · Bayesian Analysis · doi:10.1214/09-ba414 · Cited by 124 · see citing works
Sutherland, G. T., Halliday, G. M., Silburn, P. A., Mastaglia, F. L., Rowe, D. B., Boyle, R. S., O’Sullivan, J. D., Ly, T., Wilton, S. D., & Mellick, G. D. (2009) · Movement Disorders · doi:10.1002/mds.22214 · Cited by 51 · see citing works
Self-citationAn exploration of cognitive subgroups in Alzheimer’s disease
Davidson, J. E., Irizarry, M. C., Bray, B. C., Wetten, S., Galwey, N. W., Gibson, R. A., BORRIE, M. J., Delisle, R. G., Feldman, H., Hsiung, G. R., et al. (2009) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617709991160 · Cited by 47 · see citing works
Motor patterns in Parkinson's disease: A data‐driven approach
van Rooden, S. M., Jeukens‐Visser, M., Verbaan, D., Marinus, J., & van Hilten, J. J. (2009) · Movement Disorders · doi:10.1002/mds.22512 · Cited by 37 · see citing works
An enriched conjugate prior for Bayesian nonparametric inference
Wade, S., Mongelluzzo, S., & Petrone, S. (2011) · Bayesian Analysis · doi:10.1214/11-ba614 · Cited by 23 · see citing works
Bayesian latent trait modeling of migraine symptom data
Chen, C. C. M., Keith, J. M., Nyholt, D. R., et al. (2009) · Human Genetics · doi:10.1007/s00439-009-0671-4 · Cited by 21 · see citing works
White, N. M., Johnson, H., Silburn, P. A., Mellick, G. D., Dissanayaka, N. N., & Mengersen, K. L. (2010) · Statistical Methods in Medical Research · doi:10.1177/0962280210391012 · Cited by 9 · see citing works
Self-citationLeask, S., Vermunt, J. K., Done, D. J., Crow, T. J., Blows, M., & Boks, M. P. (2009) · Schizophrenia Research · doi:10.1016/j.schres.2009.09.017 · Cited by 9 · see citing works
Latent class analysis identification of syndromes in Alzheimer's disease: A Bayesian approach
Walsh, C. (2006) · Repository of the University of Ljubljana (University of Ljubljana) · doi:10.34961/4190 · Cited by 8 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- 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.
- 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.
- G. Schwartz, Estimating the dimension of a model, Annals of Statistics 6 (1978), pp. 461-464.
- 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.
Cited by
Sorted by citations
Kubota, K. J., Chen, J. A., & Little, M. A. (2016) · Movement Disorders · doi:10.1002/mds.26693 · Cited by 210 · see citing works
- Authors
- White, N., Johnson, H., Silburn, P., & Mengersen, K.
- Affiliations
- Queensland University of Technology (AU)
- Corresponding author
- White, Nicole
- Venue
- Journal of Applied Statistics
- Publisher
- Informa UK Limited
- 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
- green
- eprints.qut.edu.au
- 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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