Models for prediction of mortality from cirrhosis with special reference to artificial neural network: a critical 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.
Ghoshal
Uday Chand
UC
Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, 226014, India, ghoshal@sgpgi.ac.in.
Das
Ananya
A