Please use this identifier to cite or link to this item:
Title: A phase type survival tree model for clustering patients’ hospital length of stay
Authors: Garg, Lalit
McClean, Sally
Meenan, Brian
Millard, Peter
Keywords: Recursive partitioning
Patient compliance
Statistics -- Study and teaching
Cerebrovascular disease -- Patients
Issue Date: 2009
Publisher: ASMDA
Citation: Garg, L., McClean, S. I., Meenan, B. J., & Millard, P. H. (2009). A phase type survival tree model for clustering patients’ hospital length of stay'. XIII International Conference on Applied Stochastic Models and Data Analysis, Vilnius. 477-481.
Abstract: Clinical investigators, health professionals and managers are often interested in developing criteria for clustering patients into clinically meaningful groups according to their expected length of stay. In this paper, we propose phase-type survival trees which extend previous work on exponential survival trees. The trees are used to cluster the patients with respect to length of stay where partitioning is based on covariates such as gender, age at the time of admission and primary diagnosis code. Likelihood ratio tests are used to determine optimal partitions. The approach is illustrated using nationwide data available from the English Hospital Episode Statistics (HES) database on stroke-related patients, aged 65 years and over, who were discharged from English hospitals over a 1-year period.
ISBN: 9789955284635
Appears in Collections:Scholarly Works - FacICTCIS

Files in This Item:
File Description SizeFormat 

Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.