Identifying risk of adverse outcomes in COVID-19 patients via artificial intelligence–powered analysis of 12-lead intake electrocardiogram

BACKGROUND: Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that artificial intelligence (AI) can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could...

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Detalles Bibliográficos
Autores principales: Sridhar, Arun R., Chen (Amber), Zih-Hua, Mayfield, Jacob J., Fohner, Alison E., Arvanitis, Panagiotis, Atkinson, Sarah, Braunschweig, Frieder, Chatterjee, Neal A., Zamponi, Alessio Falasca, Johnson, Gregory, Joshi, Sanika A., Lassen, Mats C.H., Poole, Jeanne E., Rumer, Christopher, Skaarup, Kristoffer G., Biering-Sørensen, Tor, Blomstrom-Lundqvist, Carina, Linde, Cecilia M., Maleckar, Mary M., Boyle, Patrick M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719367/
https://www.ncbi.nlm.nih.gov/pubmed/35005676
http://dx.doi.org/10.1016/j.cvdhj.2021.12.003

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719367/
https://www.ncbi.nlm.nih.gov/pubmed/35005676
http://dx.doi.org/10.1016/j.cvdhj.2021.12.003

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