Transfer learning for predicting acute myocardial infarction using electrocardiograms.

ABSTRACT: At the emergency department, it is important to quickly and accurately identify patients at risk of acute myocardial infarction (AMI). One of the main tools for detecting AMI is the electrocardiogram (ECG), which can be difficult to interpret manually. There is a long history of applying machine learning algorithms to ECGs, but such algorithms are quite data hungry, and correctly labeled high-quality ECGs are difficult to obtain. Transfer learning has been a successful strategy for mitigating data requirements in other applications, but the benefits for predicting AMI are understudied. Here we show that a straightforward application of transfer learning leads to large improvements also in this domain. We pre-train models to classify sex and age using a collection of 840 k ECGs from non-che

SUBMITTER: Nystrom A 

PROVIDER: S-EPMC12578225 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature