Healthc Inform Res.  2012 Mar;18(1):29-34. 10.4258/hir.2012.18.1.29.

A Mixture of Experts Model for the Diagnosis of Liver Cirrhosis by Measuring the Liver Stiffness

• Myoung S
• Chang JH
• Song K

• KMID: 2166615
• DOI: http://doi.org/10.4258/hir.2012.18.1.29

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.

Keyword

Mixture of Experts; Classification; Liver Stiffness; Medical Decision Support

MeSH Terms

Elasticity Imaging Techniques
Humans
Learning
Liver
Liver Cirrhosis
Logistic Models

Figure

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  Figure 1 The architecture of mixture of expert.

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  Figure 2 Configured mixture of experts structure for diagnosis of liver cirrhosis.

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  Figure 3 Receiver operating characteristic (ROC) curves of the stand-alone logistic regression and mixture of experts network structure used for diagnosis of liver cirrhosis.

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