TY - GEN

T1 - Bayesian networks layer model to represent anesthetic practice

AU - Shiratori, Naruhiko

AU - Okude, Naohito

PY - 2007/12/1

Y1 - 2007/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=40949160198&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=40949160198&partnerID=8YFLogxK

U2 - 10.1109/ICSMC.2007.4414061

DO - 10.1109/ICSMC.2007.4414061

M3 - Conference contribution

AN - SCOPUS:40949160198

SN - 1424409918

SN - 9781424409914

T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

SP - 674

EP - 679

BT - 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007

T2 - 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007

Y2 - 7 October 2007 through 10 October 2007

ER -