Real-world data-driven early warning system for risk-stratified liver injury in hospitalized COVID-19 patients-Machine learning models for clinical decision support.

ABSTRACT:

Objective

To develop and validate a real-world evidence-driven early warning system for the risk-stratified prediction of coronavirus disease 2019 (COVID-19)-associated hepatic dysfunction in hospitalized patients, leveraging interpretable machine learning models to provide clinically actionable decision support for timely intervention.

Methods

A retrospective single-center cohort study was conducted utilizing high-resolution electronic health records (EHRs) from 983 hospitalized COVID-19 patients. Clinical features (e.g., laboratory results, medication exposures, and disease progression markers) were systematically analyzed. To mitigate class imbalance, we employed the Synthetic Minority Oversampling TEchnique (SMOTE) prior to model development. Thirteen distinct machine le

SUBMITTER: Xiong Y 

PROVIDER: S-EPMC12441008 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature