Cardiac and extracardiac discharge diagnosis prediction from emergency
  department ECGs using deep learning
Nils Strodthoff Juan Miguel Lopez Alcaraz Wilhelm Haverkamp
Current deep learning algorithms designed for automatic ECG analysis have
exhibited notable accuracy. However, akin to traditional electrocardiography,
they tend to be narrowly focused and typically address a singular diagnostic
condition. In this study, we specifically demonstrate the capability of a
single model to predict a diverse range of both cardiac and non-cardiac
discharge diagnoses based on a sole ECG collected in the emergency department.
Among the 1,076 hierarchically structured ICD codes considered, our model
achieves an AUROC exceeding 0.8 in 439 of them. This underscores the models
proficiency in handling a wide array of diagnostic scenarios. We emphasize the
potential of utilizing this model as a screening tool, potentially integrated
into a holistic clinical decision support system for efficiently triaging
patients in the emergency department. This research underscores the remarkable
capabilities of comprehensive ECG analysis algorithms and the extensive range
of possibilities facilitated by the open MIMIC-IV-ECG dataset. Finally, our
data may play a pivotal role in revolutionizing the way ECG analysis is
performed, marking a significant advancement in the field.