May 14, 2024, 4:44 a.m. | Nils Strodthoff, Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp

cs.LG updates on

arXiv:2312.11050v2 Announce Type: replace-cross
Abstract: 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 exploratory study, we specifically investigate 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. We find that 253, 81 cardiac, and 172 non-cardiac, …

abstract accuracy algorithms analysis arxiv cs.lg current deep learning deep learning algorithms diagnostic eess.sp emergency exploratory however prospects replace screening singular study tool type

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