Machine learning-based prediction of acute mortality in emergency department patients using twelve-lead electrocardiogram.

ABSTRACT:

Background

The risk of mortality is relatively high among patients who visit the emergency department (ED), and stratifying patients at high risk can help improve medical care. This study aimed to create a machine-learning model that utilizes the standard 12-lead ECG to forecast acute mortality risk in ED patients.

Methods

The database included patients who visited the EDs and underwent standard 12-lead ECG between October 2007 and December 2017. A convolutional neural network (CNN) ECG model was developed to classify survival and mortality using 12-lead ECG tracings acquired from 345,593 ED patients. For machine learning model development, the patients were randomly divided into training, validation and testing datasets. The performance of the mortality risk prediction in t

SUBMITTER: Chang PC 

PROVIDER: S-EPMC10641780 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature