Work

An Explainable Machine Learning Approach Reveals Prognostic Significance of Right Ventricular Dysfunction in Nonischemic Cardiomyopathy

Year: 2022

Type: article

Study design: Observational Study

Source: JACC. Cardiovascular imaging

DOI: 10.1016/j.jcmg.2021.11.029

Authors: Ahmed S. Fahmy, Ibolya Csecs, Arghavan Arafati, Salah Assana, Tuyen T. Yankama, Talal Al-Otaibi, Jennifer Rodriguez, Yi-Yun Chen, Long H. Ngo, Warren J. Manning, Raymond Y. Kwong, Reza Nezafat

Institutions: Beth Israel Deaconess Medical Center, Harvard University, Albert Einstein College of Medicine, Jacobi Medical Center, Brigham and Women's Hospital

Language: English

Topic: Cardiomyopathy and Myosin Studies

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Institutes of Health, National Heart, Lung, and Blood Institute

Awards: Cardiac MR-Based Risk Stratification for Heart Failure and Atrial Fibrillation in HCM, Cardiovascular MRI Characterization of the Arrhythmogenic Heart in Nonischemic Cardiomyopathy, Imaging Markers of Subclinical Cardiotoxicity in Breast Cancer, Myocardial Tissue Characterization with MR Relaxometry in Heart Failure, Gadolinium Free Cardiac MR Imaging of Scar and Fibrosis

OpenAlex ID: W4205434620

FWCI: 6.681

Cites: 25

Cited by: 49

Related to: 10