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A mixture of experts model for the diagnosis of liver cirrhosis by measuring the liver stiffness.

Authors
 Sungmin Myoung  ;  Ji Hong Chang  ;  Kijun Song 
Citation
 HEALTHCARE INFORMATICS RESEARCH, Vol.18(1) : 29-34, 2012 
Journal Title
 HEALTHCARE INFORMATICS RESEARCH 
ISSN
 2093-3681 
Issue Date
2012
Keywords
Classification ; Liver Stiffness ; Medical Decision Support ; Mixture of Experts
Abstract
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.
Files in This Item:
T201203474.pdf Download
DOI
22509471
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
Yonsei Authors
Song, Ki Jun(송기준) ORCID logo https://orcid.org/0000-0003-2505-4112
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/90342
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