Interpretable machine learning model for predicting covert hepatic encephalopathy in patients with cirrhosis: a multicenter study.

ABSTRACT:

Background and aim

Covert hepatic encephalopathy (CHE) is a neurocognitive complication affecting 40.9-50.4% of patients with cirrhosis. It often remains undiagnosed owing to its subclinical nature and the limitations of existing diagnostic tools, which are constrained by subjectivity, variable sensitivity, and limited accessibility. This study aims to develop and validate interpretable machine learning (ML) models for predicting CHE in patients with cirrhosis using multidimensional clinical and lifestyle data.

Methods

This retrospective study included 503 patients with liver cirrhosis from 16 medical centers in China. CHE was diagnosed using the psychometric hepatic encephalopathy score and EncephalApp Stroop tests. Recursive feature elimination and Pearson's correlation an

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12685911 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature