Construction and validation of a machine learning-based prediction model for short-term mortality in critically ill patients with liver cirrhosis

This publication has 58 references indexed in Scilit:

• Federated machine learning for predicting acute kidney injury in critically ill patients: a multicenter study in Taiwan
  Health Information Science and Systems, 2023
• Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost
  Computers, Environment and Urban Systems, 2022
• Classification of blood pressure in critically ill patients using photoplethysmography and machine learning
  Computer Methods and Programs in Biomedicine, 2021
• Development, Validation, and Evaluation of a Simple Machine Learning Model to Predict Cirrhosis Mortality
  JAMA Network Open, 2020
• Increased Survival for Patients With Cirrhosis and Organ Failure in Liver Intensive Care and Validation of the Chronic Liver Failure–Sequential Organ Failure Scoring System
  Clinical Gastroenterology and Hepatology, 2015
• Model for end-stage liver disease exceptions in the context of the french model for end-stage liver disease score–based liver allocation system
  Liver Transplantation, 2011
• Portal hypertension and variceal bleeding—Unresolved issues. Summary of an American Association for the study of liver diseases and European Association for the study of the liver single-topic conference
  Hepatology, 2008
• RIFLE classification can predict short-term prognosis in critically ill cirrhotic patients
  Intensive Care Medicine, 2007
• Improved survival after variceal bleeding in patients with cirrhosis over the past two decades
  Hepatology, 2004
• A New Simplified Acute Physiology Score (SAPS II) Based on a European/North American Multicenter Study
  JAMA, 1993