Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.
Original languageEnglish
Title of host publication22nd IEEE International Symposium on Computer-Based Medical Systems, 2009. CBMS 2009. Proceedings.
Pages553-558
Number of pages6
DOIs
Publication statusPublished - 2009
Event22nd IEEE International Symposium on Computer-Based Medical Systems - Albuquerque, Nm, United States
Duration: 01 Aug 200901 Aug 2009

Publication series

Name
PublisherIEEE International Symposium on Computer-Based Medical Systems
ISSN (Print)1063-7125

Conference

Conference22nd IEEE International Symposium on Computer-Based Medical Systems
CountryUnited States
CityAlbuquerque, Nm
Period01/08/200901/08/2009

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