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Current deep learning algorithms designed for automatic ECG analysis haveexhibited notable accuracy. However, akin to traditional electrocardiography,they tend to be narrowly focused and typically address a singular diagnosticcondition. In this study, we specifically demonstrate the capability of asingle model to predict a diverse range of both cardiac and non-cardiacdischarge diagnoses based on a sole ECG collected in the emergency department.Among the 1,076 hierarchically structured ICD codes considered, our modelachieves an AUROC exceeding 0.8 in 439 of them. This underscores the modelsproficiency in handling a wide array of diagnostic scenarios. We emphasize thepotential of utilizing this model as a screening tool, potentially integratedinto a holistic clinical decision support system for efficiently triagingpatients in the emergency department. This research underscores the remarkablecapabilities of comprehensive ECG analysis algorithms and the extensive rangeof possibilities facilitated by the open MIMIC-IV-ECG dataset. Finally, ourdata may play a pivotal role in revolutionizing the way ECG analysis isperformed, marking a significant advancement in the field.

Cardiac and extracardiac discharge diagnosis prediction from emergency department ECGs using deep learning