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In our previous work we proposed a phase type survival tree method for clustering patients into clinically meaningful patient groups. We also discussed its extension which uses the survival tree based analysis for patient pathway prognostication and to examine the relationship between length of stay in hospital and destination on discharge. The current paper illustrates how phase type survival tree...
Survival tree based analysis is a powerful method of prognostication and determining clinically meaningful patient groups from a given dataset of patients' length of stay. In our previous work [1, 2] we proposed a phase type survival tree method for clustering patients into homogeneous groups with respect to their length of stay where partitioning is based on covariates representing patient characteristics...
In recent years there has been considerable interest in the possibility of improving healthcare by using ideas from manufacturing and engineering, such as lean thinking. In this paper we describe a lean systems framework for healthcare improvement, where we propose to discover high-level patient pathways using a Markov phase-type model that employs readily available, patient administrative data. Such...
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