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Models for prediction of mortality from cirrhosis with special reference to artificial neural network: a critical review.
MedLine Citation:
PMID:  19669277     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Prediction of mortality of patients with cirrhosis of liver, a common and potentially fatal disease, is important for timely listing of patients for liver transplantation. The Child-Pugh scoring system has been widely used for predicting the outcome of liver cirrhosis. The Model for End-Stage Liver Disease (MELD) score has recently become popular for prediction of short-term mortality for organ allocation. A few studies that evaluated artificial neural network (ANN)-based model for prediction of outcome of cirrhosis of liver in terms of mortality have consistently shown it to be superior to Child-Pugh scoring and logistic regression-based models; it is worth noting that MELD score is also derived using the logistic regression model. Due to the inherent ability of neural network-based systems in identifying complex nonlinear interactions, ANN-based models are expected to perform better than most linear models, such as regression-based models. More studies are needed on ANN-based models for prediction of mortality of patients with cirrhosis of liver and its value in prioritization of organ allocation for treatment of patients with cirrhosis of liver.
Authors:
Uday Chand Ghoshal; Ananya Das
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Publication Detail:
Type:  Journal Article     Date:  2007-11-27
Journal Detail:
Title:  Hepatology international     Volume:  2     ISSN:  1936-0533     ISO Abbreviation:  Hepatol Int     Publication Date:  2008 Mar 
Date Detail:
Created Date:  2009-08-11     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101304009     Medline TA:  Hepatol Int     Country:  United States    
Other Details:
Languages:  eng     Pagination:  31-8     Citation Subset:  -    
Affiliation:
Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, 226014, India, ghoshal@sgpgi.ac.in.
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