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Title: Clustering patient length of stay using mixtures of Gaussian models and phase type distributions
Authors: Garg, Lalit
McClean, Sally
Meenan, Brian
El-Darzi, Elia
Millard, Peter
Keywords: Magnetic resonance imaging
Speech processing systems
Random noise theory
Ambient sounds
Issue Date: 2009
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Garg, L., McClean, S., Meenan, B., El-Darzi, E., & Millard, P. (2009). Clustering patient length of stay using mixtures of Gaussian models and phase type distributions. 22nd IEEE International Symposium on Computer-Based Medical Systems, Albuquerque. 1-7.
Abstract: Gaussian mixture distributions and Coxian phase type distributions have been popular choices model based clustering of patients’ length of stay data. This paper compares these models and presents an idea for a mixture distribution comprising of components of both of the above distributions. Also a mixed distribution survival tree is presented. A stroke dataset available from the English Hospital Episode Statistics database is used as a running example.
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