License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.SCOR.2014.89
URN: urn:nbn:de:0030-drops-46727
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2014/4672/
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Mesgarpour, Mohsen ; Chaussalet, Thierry ; Chahed, Salma

A Review of Dynamic Bayesian Network Techniques with Applications in Healthcare Risk Modelling

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Abstract

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

BibTeX - Entry

@InProceedings{mesgarpour_et_al:OASIcs:2014:4672,
  author =	{Mohsen Mesgarpour and Thierry Chaussalet and Salma Chahed},
  title =	{{A Review of Dynamic Bayesian Network Techniques with Applications in Healthcare Risk Modelling}},
  booktitle =	{4th Student Conference on Operational Research},
  pages =	{89--100},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-67-5},
  ISSN =	{2190-6807},
  year =	{2014},
  volume =	{37},
  editor =	{Pedro Crespo Del Granado and Martim Joyce-Moniz and Stefan Ravizza},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2014/4672},
  URN =		{urn:nbn:de:0030-drops-46727},
  doi =		{10.4230/OASIcs.SCOR.2014.89},
  annote =	{Keywords: Healthcare Modelling, Dynamic Bayesian Network, Predictive Risk Modelling, Time-Varying, Hospital Administrative Data}
}

Keywords: Healthcare Modelling, Dynamic Bayesian Network, Predictive Risk Modelling, Time-Varying, Hospital Administrative Data
Collection: 4th Student Conference on Operational Research
Issue Date: 2014
Date of publication: 06.08.2014

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