Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/24519
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. |
URI: | https://www.um.edu.mt/library/oar//handle/123456789/24519 |
ISBN: | 9789955284635 |
Appears in Collections: | Scholarly Works - FacICTCIS |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
A_PHASE_TYPE_SURVIVAL_TREE_MODEL_FOR_CLUSTERING_PA.pdf | 303.27 kB | Adobe PDF | View/Open |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.