Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study.

ABSTRACT:

Background

Most artificial intelligence-based research on acute kidney injury (AKI) prediction has focused on intensive care unit settings, limiting their generalizability to general wards. The lack of standardized AKI definitions and reliance on intensive care units further hinder the clinical applicability of these models.

Objective

This study aims to develop and validate a machine learning-based framework to assist in managing AKI and acute kidney disease (AKD) in general ward patients, using a refined operational definition of AKI to improve predictive performance and clinical relevance.

Methods

This retrospective multicenter cohort study analyzed electronic health record data from 3 hospitals in South Korea. AKI and AKD were defined using a refined version of the

SUBMITTER: Cho NJ 

PROVIDER: S-EPMC11962325 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature