Construction and validation of a machine learning-based model predicting early readmission in patients with decompensated cirrhosis: a prospective two-center cohort study.

ABSTRACT:

Background

Early 30-day readmission remains a significant burden on the socioeconomic and healthcare system in the context of decompensated cirrhosis. Early recognition and accurate identification are crucial. However, current evidence is elusive and traditional scores concerning liver disease severity are lacking specificity and sensitivity. We sought to construct and validate an explainable machine learning (ML)-based prediction model, and evaluate its prognostic implementation in patients readmitted due to acute episodes. The prediction model for discovery and validation was based on a two-center prospective investigation. Our discovery sample, comprising 636 patients with cirrhosis, was divided into a training set and a test set, with an additional cohort of 150 patients servin

SUBMITTER: Yang F 

PROVIDER: S-EPMC12462353 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature