%0 Journal Article
%T Machine learning identifies pathophysiologically and prognostically informative phenotypes among patients with mitral regurgitation undergoing transcatheter edge-to-edge repair
%A Trenkwalder, Teresa
%A Lachmann, Mark
%A Stolz, Lukas
%A Fortmeier, Vera
%A Covarrubias, Héctor Alfonso Alvarez
%A Rippen, Elena
%A Schürmann, Friederike
%A Presch, Antonia
%A von Scheidt, Moritz
%A Ruff, Celine
%A Hesse, Amelie
%A Gerçek, Muhammed
%A Mayr, N Patrick
%A Ott, Ilka
%A Schuster, Tibor
%A Harmsen, Gerhard
%A Yuasa, Shinsuke
%A Kufner, Sebastian
%A Hoppmann, Petra
%A Kupatt, Christian
%A Schunkert, Heribert
%A Kastrati, Adnan
%A Laugwitz, Karl-Ludwig
%A Rudolph, Volker
%A Joner, Michael
%A Hausleiter, Jörg
%A Xhepa, Erion
%J European Heart Journal - Cardiovascular Imaging
%V 24
%N 5
%P 574-587
%@ 2047-2404
%D 2023-02-03
%I Oxford University Press
%~ DeepDyve