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A Machine-Learning Framework to Identify Distinct Phenotypes of Aortic Stenosis Severity

Partho P. Sengupta, Sirish Shrestha, Nobuyuki Kagiyama, Yasmin S. Hamirani, Hemant Kulkarni, Naveena Yanamala, Rong Bing, Calvin Chin, Tania Pawade, David Messika–Zeitoun, Lionel Tastet, Mylène Shen, David E. Newby, Marie‐Annick Clavel, Phillippe Pibarot, Marc R. Dweck, Éric Larose, Ezéquiel Guzzetti, Mathieu Bernier, Jonathan Beaudoin, Marie Arsenault, Nancy Côté, Russell J. Everett, William Jenkins, Christophe Tribouilloy, Julien Dreyfus, Tiffany Mathieu, C. Renard, Mesut Gun, Laurent Macron, Jacob W. Sechrist, Joan M. Lacomis, Virginia Nguyen, Laura Galián-Gay, Hug Cuéllar Calabria, Ioannis Ntalas, Bernard Prendergast, Ronak Rajani, Arturo Evangelista, João L. Cavalcante

2021JACC. Cardiovascular imaging87 citationsDOIOpen Access PDF

Topics & Concepts

MedicineInternal medicineCohortCardiologyMagnetic resonance imagingStenosisSeverity of illnessAortic valve replacementRadiologyCardiac Valve Diseases and TreatmentsCardiovascular Function and Risk FactorsCardiac Imaging and Diagnostics
A Machine-Learning Framework to Identify Distinct Phenotypes of Aortic Stenosis Severity | Litcius