Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG.

ABSTRACT:

Background

Patients with end-stage renal disease (ESRD) especially those undergoing dialysis have a high prevalence of hyperkalemia, which must be detected and treated immediately. But the initial symptoms of hyperkalemia are insidious, and traditional laboratory serum potassium concentration testing takes time. Therefore, rapid and real-time measurement of serum potassium is urgently needed. In this study, different machine learning methods were used to make rapid predictions of different degrees of hyperkalemia by analyzing the ECG.

Methods

A total of 1024 datasets of ECG and serum potassium concentrations were analyzed from December 2020 to December 2021. The data were scaled into training and test sets. Different machine learning models (LR, SVM, CNN, XGB, Adaboost) were

SUBMITTER: Xu D 

PROVIDER: S-EPMC10197982 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature