Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients.

ABSTRACT: Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settings, deep learning-based models and especially convolutional deep models have shown promise in electrocardiogram (ECG) classification, but there is a lack of high-performing models for the diagnosis of myocardial infarction in real-world scenarios. We aimed to train and validate a deep learning model using ECGs to predict myocardial infarction in real-world emergency department patients. We studied emergency department patients in the Stockholm region between 2007 and 2016 that had an ECG obtained because of their presenting complaint. We developed a deep neural network based on convolutional layers similar to a residual network. Inputs to the model were ECG tracing, age, and sex; and outputs

SUBMITTER: Gustafsson S 

PROVIDER: S-EPMC9666471 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature