Machine learning identifies pathophysiologically and prognostically informative phenotypes among patients with mitral regurgitation undergoing transcatheter edge-to-edge repair.

Teresa Trenkwalder Mark LachmannLukas StolzVera FortmeierHéctor Alfonso Alvarez CovarrubiasElena RippenFriederike SchürmannAntonia PreschMoritz von ScheidtCeline RuffAmelie HesseMuhammed GerçekNicola Patrick MayrIlka OttTibor SchusterGerhard HarmsenShinsuke YuasaSebastian KufnerPetra HoppmannChristian Kupatt Heribert Schunkert Adnan Kastrati Karl-Ludwig Laugwitz Volker RudolphMichael JonerJörg HausleiterErion Xhepa

Published in: European heart journal. Cardiovascular Imaging (2023)

ML-enabled phenotyping captures the complexity of extra-mitral valve cardiac damage, which does not necessarily occur in a sequential fashion. This novel phenotyping approach can refine risk stratification in patients undergoing MV TEER in the future.

Keyphrases

mitral valve
machine learning
patients undergoing
high throughput
left ventricular
left atrial
oxidative stress
current status
genome wide
artificial intelligence
big data
heart failure
gene expression
deep learning