Cikes, MajaSanchez Martinez, SergioClaggett, BrianDuchateau, NicolasPiella Fenoy, GemmaButakoff, ConstantinePouleur, Anne CatherineKnappe, DoritBiering‐Sørensen, TorKutyifa, ValentinaMoss, ArthurStein, KennethSolomon, Scott D.Bijnens, Bart2019-03-262019Cikes M, Sanchez‐Martinez S, Claggett B, Duchateau N, Piella Fenoy G, Butakoff C, Pouleur AC, Knappe D, Biering‐Sørensen T, Kutyifa V, Moss A, Stein K, Solomon SD, Bijnens B. Machine-learning based phenogrouping in heart failure to identify responders to resynchronization therapy. Eur J Heart Fail. 2019 Jan;21(1):74-85. DOI: 10.1002/ejhf.13331879-0844http://hdl.handle.net/10230/36970We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT).application/pdfengThis is the peer reviewed version of the following article: Cikes M, Sanchez‐Martinez S, Claggett B, Duchateau N, Piella Fenoy G, Butakoff C, Pouleur AC, Knappe D, Biering‐Sørensen T, Kutyifa V, Moss A, Stein K, Solomon SD, Bijnens B. Machine-learning based phenogrouping in heart failure to identify responders to resynchronization therapy. Eur J Heart Fail. 2019 Jan;21(1):74-85, which has been published in final form at http://dx.doi.org/10.1002/ejhf.1333. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1002/ejhf.1333Machine learningHeart failurePersonalized medicineEchocardiographyCardiac resynchronization therapyinfo:eu-repo/semantics/openAccess