Sanchez Martinez, SergioDuchateau, NicolasErdei, TamasKunszt, GaborAakhus, SvendDegiovanni, AnnaMarino, PaoloCarluccio, ErbertoPiella Fenoy, GemmaFraser, Alan G.Bijnens, Bart2019-03-262019-03-262018Sanchez-Martinez S, Duchateau N, Erdei T, Kunszt G, Aakhus S, Degiovanni A, Marino P, Carluccio E, Piella G, Fraser AG, Bijnens BH. Machine learning analysis of left ventricular function to characterize heart failure with preserved ejection fraction. Circ Cardiovasc Imaging. 2018 Apr 16;11(4):e007138. DOI: 10.1161/CIRCIMAGING.117.0071381941-9651http://hdl.handle.net/10230/36968Current diagnosis of heart failure with preserved ejection fraction (HFpEF) is suboptimal. We tested the hypothesis that comprehensive machine learning (ML) of left ventricular function at rest and exercise objectively captures differences between HFpEF and healthy subjects.application/pdfeng© American Hearth Association http://dx.doi.org/10.1161/CIRCIMAGING.117.007138info:eu-repo/semantics/articlehttp://dx.doi.org/10.1161/CIRCIMAGING.117.007138EchocardiographyMachine learningEarly diagnosisHeart failureDiastolicUltrasonographyDopplerStressinfo:eu-repo/semantics/openAccess