A Discrete Conditional Phase-Type Model Utilising a Survival Tree for the Identification of Elderly Patient Cohorts and Their Subsequent Prediction of Length of Stay in Hospital

Andrew S. Gordon, Adele H. Marshall, Mariangela Zenga

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

1 Citation (Scopus)

Abstract

Health care providers continue to feel the pressure in providing adequate care for an increasing elderly population. If length of stay patterns for elderly patients in care can be captured through analytical modelling, then accurate predictions may be made on when they are expected to leave hospital. The Discrete Conditional Phase-type (DC-Ph) model is an effective technique through which length of stay in hospital can be modelled and consists of both a conditional and a process component. This research expands the DC-Ph model by introducing a survival tree as the conditional component, whereby covariates are used to partition patients into cohorts based on their distribution of length of stay in hospital. The Coxian phase-type distribution is then used to model the length of stay for patients belonging to each cohort. A demonstration of how patient length of stay may be predicted for new admissions using this methodology is then given. This tool has the benefit of providing an aid to the decision making processes undertaken by hospital managers and has the potential to result in the more effective allocation of hospital resources. Hospital admission data from the Lombardy region of Italy is used as a case-study.

Original languageEnglish
Title of host publicationProceedings - IEEE 29th International Symposium on Computer-Based Medical Systems, CBMS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages259-264
Number of pages6
ISBN (Electronic)9781467390361
DOIs
Publication statusPublished - 18 Aug 2016
Event29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016 - Belfast, United Kingdom
Duration: 20 Jun 201623 Jun 2016

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Volume2016-August
ISSN (Print)1063-7125

Conference

Conference29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016
CountryUnited Kingdom
CityBelfast
Period20/06/201623/06/2016

Keywords

  • Coxian phase-type distribution
  • Length of stay
  • Survival analysis
  • Survival tree

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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    Gordon, A. S., Marshall, A. H., & Zenga, M. (2016). A Discrete Conditional Phase-Type Model Utilising a Survival Tree for the Identification of Elderly Patient Cohorts and Their Subsequent Prediction of Length of Stay in Hospital. In Proceedings - IEEE 29th International Symposium on Computer-Based Medical Systems, CBMS 2016 (pp. 259-264). [7545997] (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2016-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CBMS.2016.17