Identifying Patients With Heart Failure Who Are Susceptible to De Novo Acute Kidney Injury: Machine Learning Approach.

ABSTRACT:

Background

Studies have shown that more than half of patients with heart failure (HF) with acute kidney injury (AKI) have newonset AKI, and renal function evaluation markers such as estimated glomerular filtration rate are usually not repeatedly tested during the hospitalization. As an independent risk factor, delayed AKI recognition has been shown to be associated with the adverse events of patients with HF, such as chronic kidney disease and death.

Objective

The aim of this study is to develop and assess of an unsupervised machine learning model that identifies patients with HF and normal renal function but who are susceptible to de novo AKI.

Methods

We analyzed an electronic health record data set that included 5075 patients admitted for HF with normal renal functi

SUBMITTER: Hong C 

PROVIDER: S-EPMC9617187 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature