Document Detail

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    
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.
Uday Chand Ghoshal; Ananya Das
Related Documents :
19959327 - Data splitting for artificial neural networks using som-based stratified sampling.
12756977 - Reproductive intentions of the newlywed bulgarian families--artificial neural network a...
20801027 - An artificial intelligence approach to bacillus amyloliquefaciens ccmi 1051 cultures: a...
12111397 - Structure-cytotoxicity relationships for a series of hept derivatives.
24296697 - Identifying the principal modes of variation in human thoracic aorta morphology.
22837827 - Early stages of divergence: phylogeography, climate modeling, and morphological differe...
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:  -    
Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, 226014, India,
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Previous Document:  Diagnostic accuracy of tumor markers for hepatocellular carcinoma: a systematic review.
Next Document:  Genome-wide transcriptome expression in the liver of a mouse model of high carbohydrate diet-induced...