Enhancing end-stage renal disease outcome prediction: a multisourced data-driven approach.

ABSTRACT:

Objectives

To improve prediction of chronic kidney disease (CKD) progression to end-stage renal disease (ESRD) using machine learning (ML) and deep learning (DL) models applied to integrated clinical and claims data with varying observation windows, supported by explainable artificial intelligence (AI) to enhance interpretability and reduce bias.

Materials and methods

We utilized data from 10 326 CKD patients, combining clinical and claims information from 2009 to 2018. After preprocessing, cohort identification, and feature engineering, we evaluated multiple statistical, ML and DL models using 5 distinct observation windows. Feature importance and SHapley Additive exPlanations (SHAP) analysis were employed to understand key predictors. Models were tested for robustness, cli

SUBMITTER: Li Y 

PROVIDER: S-EPMC12758457 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature