Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.

ABSTRACT:

Background

Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. We aimed to investigate whether neural network-derived ECG features could be used to predict future cardiovascular disease and mortality and have phenotypic and genotypic associations.

Methods

We extracted 5120 neural network-derived ECG features from an artificial intelligence-enabled ECG model trained for 6 simple diagnoses and applied unsupervised machine learning to identify 3 phenogroups. Using the identified phenogroups, we externally validated our findings in 5 diverse cohorts from the United States, Brazil, and the United

SUBMITTER: Sau A 

PROVIDER: S-EPMC7616866 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature