Applying BN in CBR Adaptation-Guided Retrieval for Medical Diagnosis
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- 발행기관
- 보안공학연구지원센터(IJHIT) 바로가기
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- 간행물
- International Journal of Hybrid Information Technology 바로가기
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- 통권
- Vol.5 No.2 (2012.04)바로가기
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- 페이지
- pp.41-56
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A208172
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- The Case Based Reasoning (CBR) is an approach of solving problem which is based on the reuse, by analogy, of past experiences called case. It is based on the retrieval and adaptation of the old solutions to the new problems. This paper presents a Bayesian adaptation-Guided Retrieval phase for a CBR applied to the diagnosis of hepatic pathologies. The main idea consists in a modelling the case base by a Bayesian Network (BN). Its are excellent tools for modelling the uncertainty in terms of their clear graphic representation as well as the conditional probabilities laws defined on a graph. We are interested to retrieval and adaptation phases. The retrieval phase consists of selecting the most similar case of log linear model by the considering Bayesian Network as a log-linear model on the simplification of the probability. The adaptation phase means modifying solutions of retrieved cases to fit the current problem. The dependence between these two phases is defined by two measures: a similarity measure and an adaptation measure. The objective of this dependence is to guarantee the retrieved case which is the easiest to adapt and improve the performance of CBR. An example of the diagnosis of the hepatic pathologies will illustrate the presented approach.
목차
Abstract
1. Introduction
2. Related works
3. CBR Cycle
4. Presentation of Bayesian network
4.1. Definition of Bayesian Network
4.2. Inference
5. Integrating Bayesian Network in CBR
5.1 Network Variables Definition
5.2 Definition of Weight
5.3 The Case Base Architecture
5.4 Bayesian Case Description
5.5 Log linear Models
6. The Proposed Retrieval Phase
6.1 Process of Initialization
6.2 Process of Propagation (Extension of Pearl Algorithm)
6.3 Process of Retrieval
7. The Proposed Adaptation Phase
7.1 The Adaptation Measure Definition
7.2 Algorithm of the Proposed Adaptation Phase
7.3 Illustration of Adaptation Phase
8. Discussion
9. Conclusion and Perspectives
References
키워드
Case Based Reasoning (CBR) adaptation adaptation-guided retrieval Bayesian Network (BN) medical diagnosis.저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJHIT) [Science & Engineering Research Support Center, Republic of Korea(IJHIT)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Hybrid Information Technology
- 간기
- 격월간
- pISSN
- 1738-9968
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
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