Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Care

Autor:	Strodthoff, Nils, Alcaraz, Juan Miguel Lopez, Haverkamp, Wilhelm
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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, ICD codes can be reliably predicted in the sense of exceeding an AUROC score of 0.8 in a statistically significant manner. This underscores the model's proficiency in handling a wide array of cardiac and non-cardiac diagnostic scenarios which demonstrates potential as a screening tool for diverse medical encounters. Comment: Accepted version EHJDH. 30 pages, 6 figures, code available under https://github.com/AI4HealthUOL/ECG-MIMIC
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.11050 Zobrazit plný text záznamu View this record from Arxiv