지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2012.1
- 수록면
- 29 - 34 (6page)
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초록· 키워드
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Objectives: The mixture-of-experts (ME) network uses a modular type of neural network architecture optimized for supervised learning. This model has been applied to a variety of areas related to pattern classification and regression. In this research, we applied a ME model to classify hidden subgroups and test its significance by measuring the stiffness of the liver as associated with the development of liver cirrhosis. Methods: The data used in this study was based on transient elastography (Fibroscan) by Kim et al. We enrolled 228 HBsAg-positive patients whose liver stiffness was measured by the Fibroscan system during six months. Statistical analysis was performed by R-2.13.0. Results: A classical logistic regression model together with an expert model was used to describe and classify hidden subgroups. The performance of the proposed model was evaluated in terms of the classification accuracy, and the results confirmed that the proposed ME model has some potential in detecting liver cirrhosis. Conclusions: This method can be used as an important diagnostic decision support mechanism to assist physicians in the diagnosis of liver cirrhosis in patients.
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참고문헌 (11)
참고문헌 신청
Corchado JM. / 2009 / Model of experts for decision support in the diagnosis of leukemia patients / Artif Intell Med 46 : 179 ~ 200
Jacobs RA. / 1991 / Adaptive mixtures of local experts / Neural Comput 3 : 79 ~ 87
Jordan MI. / 1994 / Hierarchical mixtures of experts and the EM algorithm / Neural Comput 6 : 181 ~ 214
Marsland S. / 2009 / Machine learning : an algorithmic perspective / CRC Press
McCullagh P. / 1983 / Generalized linear models / Champman and Hall
Jacobs RA. / 1991 / Adaptive mixtures of local experts / Neural Comput 3 : 79 ~ 87
Jordan MI. / 1994 / Hierarchical mixtures of experts and the EM algorithm / Neural Comput 6 : 181 ~ 214
Marsland S. / 2009 / Machine learning : an algorithmic perspective / CRC Press
McCullagh P. / 1983 / Generalized linear models / Champman and Hall
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