Bayesian networks layer model to represent anesthetic practice

Naruhiko Shiratori, Naohito Okude

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

6 Citations (Scopus)

Abstract

This paper shows how to represent an anesthetic practice using bayesian networks layer model. There are three required points to represent anesthetic practice in operation room: multidimensionality, dynamics, and uncertainty. Normally, some deterministic models, expert system models, are selected for representing knowledge of medical experts. However, the model can not treat uncertainty and dynamics for anesthetic points. Bayesian network and dynamic bayesian network are well known to represent uncertainty and are used in many domains. The bayesian network models, however, do not correspond to multiply dynamics, which is the point for anesthetic practice. In addition, object oriented bayesian network has good points for representing multidimensionality functions, but does not correspond to individual expression for each anesthetist. So, we propose layered bayesian network to challenge the problems for individual expression and multiply dynamics. The layered model integrates three kinds of bayesian network model to represent functions of anesthetic practice.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007
Pages674-679
Number of pages6
DOIs
Publication statusPublished - 2007 Dec 1
Event2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007 - Montreal, QC, Canada
Duration: 2007 Oct 72007 Oct 10

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Other

Other2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007
CountryCanada
CityMontreal, QC
Period07/10/707/10/10

ASJC Scopus subject areas

  • Engineering(all)

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