Machine Learning to Stratify Risk in Low-Gradient Aortic Stenosis Among Medicare Beneficiaries - 03/02/25
Doi : 10.1016/j.echo.2024.10.010
Sean W. Dooley, MD
Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
Naveena V.K. Yanamala, PhD
Division of Cardiovascular Diseases and Hypertension, Rutgers Robert Wood Johnson Medical School at New Brunswick, New Brunswick, New Jersey
Nora Al-Roub, MBBS
Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
Nicholas Spetko, MD
Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
Madeline A. Cassidy, BS, Constance Angell-James, MPH
Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
Partho P. Sengupta, MD
Rutgers Robert Wood Johnson Medical School at New Brunswick, New Brunswick, New Jersey
Jordan B. Strom, MD, MSc
Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
Harvard Medical School, Boston, Massachusetts
Plan
© 2024
American Society of Echocardiography. Publié par Elsevier Masson SAS. Tous droits réservés.
Vol 38 - N° 2
P. 129-132 - février 2025 Retour au numéro
Article précédent
- Application of Machine Learning Technology to Automate Thoracic Aorta Dimensions by Echocardiography
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- Hemodynamic Performance of the SAPIEN 3 Ultra Resilia Valve: Insights From a Propensity-Matched Analysis
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