Stage prediction of acute kidney injury in sepsis patients using explainable machine learning approaches.

ABSTRACT:

Background

Acute kidney injury (AKI) is a prevalent and serious complication among sepsis patients, closely associated with high mortality rates and substantial disease burden. Early prediction of AKI is vital for prompt and effective intervention and improved prognosis. This research seeks to construct and assess forecasting frameworks that leverage advanced machine learning algorithms to anticipate AKI progression in high-risk sepsis patients.

Methods

This study utilized the MIMIC-IV database, a large, publicly available critical care dataset containing comprehensive, de-identified electronic health records of over 70,000 ICU admissions at Beth Israel Deaconess Medical Center, to extract sepsis patient data for model training and test. Following feature selection, various

SUBMITTER: Quan Z 

PROVIDER: S-EPMC12568512 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature