Development and validation of a machine learning-based prediction model for hepatorenal syndrome in liver cirrhosis patients using MIMIC-IV and eICU databases.

ABSTRACT: Hepatorenal syndrome (HRS) is a key contributor to poor prognosis in liver cirrhosis. This study aims to leverage the database to build a predictive model for early identification of high-risk patients. From two sizable public databases, we retrieved pertinent information about the cirrhosis patients' therapies, comorbidities, laboratory results, and demographics. Patients from the eICU database served as a test set for external validation, while patients from the MIMIC database were divided into training and validation groups. Variables were screened using LASSO regression, Extreme Gradient Boosting (XG Boost), and Random Forest (RF). Core risk factors were determined from the intersection of the three methods. A predictive model was constructed using multivariable logistic regression and

SUBMITTER: Yao F 

PROVIDER: S-EPMC11751441 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature