Machine learning identifies pathophysiologically and prognostically informative phenotypes among patients with mitral regurgitation undergoing transcatheter edge-to-edge repair.
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H. Schunkert | M. Joner | A. Kastrati | J. Hausleiter | S. Yuasa | S. Kufner | K. Laugwitz | V. Rudolph | C. Kupatt | E. Xhepa | P. Hoppmann | N. Mayr | M. von Scheidt | L. Stolz | H. Covarrubias | M. Lachmann | T. Trenkwalder | M. Gerçek | V. Fortmeier | G. Harmsen | F. Schürmann | I. Ott | A. Presch | E. Rippen | T. Schuster | Amelie Hesse | C. Ruff