Machine-learning based phenogrouping in heart failure to identify responders to resynchronization therapy

  • Authors
  • 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
  • UPF authors
  • PIELLA FENOY, GEMA; SANCHEZ MARTINEZ, SERGIO; BIJNENS, BART H;
  • Type
  • Scholarly articles
  • Journal títle
  • European Journal of Heart Failure
  • Publication year
  • 2019
  • Volume
  • 21
  • Number
  • 1
  • Pages
  • 74-85
  • ISSN
  • 1388-9842
  • Publication State
  • Published
  • Abstract
  • We 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).
  • Complete citation
  • 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. European Journal of Heart Failure 2019; 21(1): 74-85.
Bibliometric indicators
  • 40 times cited Scopus
  • 35 times cited WOS
  • Índex Scimago de 5.556(2019)