A Mixture of Experts Model for the Diagnosis of Liver Cirrhosis by Measuring the Liver Stiffness / 대한의료정보학회지
Healthcare Informatics Research
;
: 29-34, 2012.
Artigo
em Inglês
| WPRIM
| ID: wpr-155527
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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Modelos Logísticos
/
Técnicas de Imagem por Elasticidade
/
Aprendizagem
/
Fígado
/
Cirrose Hepática
Tipo de estudo:
Estudo diagnóstico
/
Estudo prognóstico
/
Fatores de risco
Limite:
Humanos
Idioma:
Inglês
Revista:
Healthcare Informatics Research
Ano de publicação:
2012
Tipo de documento:
Artigo
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