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 language | English |
|---|---|
| Title of host publication | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
| Pages | 674-679 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2007 |
| Event | 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007 - Montreal, QC, Canada Duration: 2007 Oct 7 → 2007 Oct 10 |
Other
| Other | 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007 |
|---|---|
| Country | Canada |
| City | Montreal, QC |
| Period | 07/10/7 → 07/10/10 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Bayesian networks layer model to represent anesthetic practice. / Shiratori, Naruhiko; Okude, Naohito.
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2007. p. 674-679 4414061.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Bayesian networks layer model to represent anesthetic practice
AU - Shiratori, Naruhiko
AU - Okude, Naohito
PY - 2007
Y1 - 2007
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
SP - 674
EP - 679
BT - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ER -