Development of 365-day Life Expectancy Models: Application of Machine Learning Methods to a Prospective Study of Critically Ill Cirrhotic Patients (Preprint)

Abstract

Background: The mortality rate of cirrhotic patients in intensive care units (ICUs) is usually high. Analyzing high dimensional data and incorporating accurate life expectancy indices using machine learning methods may improve the accuracy of the prediction of the long-term prognosis of critically ill cirrhotic patients. Objective: To develop highly accurate 365-day candidate life expectancy models based on machine learning methods, and compare their accuracy with CLIF-SOFA scores in critically ill cirrhotic patients.