A machine learning-based approach for predicting renal function recovery in general ward patients with acute kidney injury.

ABSTRACT:

Background

Acute kidney injury (AKI) is a significant challenge in healthcare. While there are considerable researches dedicated to AKI patients, a crucial factor in their renal function recovery, is often overlooked. Thus, our study aims to address this issue through the development of a machine learning model to predict restoration of kidney function in patients with AKI.

Methods

Our study encompassed data from 350,345 cases, derived from three hospitals. AKI was classified in accordance with the Kidney Disease: Improving Global Outcomes. Criteria for recovery were established as either a 33% decrease in serum creatinine levels at AKI onset, which was initially employed for the diagnosis of AKI. We employed various machine learning models, selecting 43 pertinent features f

SUBMITTER: Cho NJ 

PROVIDER: S-EPMC11237326 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature