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

Journal: European journal of heart failure
Published Date: Oct 17, 2018

Abstract

AIMS: 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).

Authors

  • Maja Cikes
    Department of Cardiovascular Diseases, University of Zagreb School of Medicine, and University Hospital Center Zagreb, Zagreb, Croatia.
  • Sergio Sanchez-Martinez
    Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain. Electronic address: sergio.sanchezm@upf.edu.
  • Brian Claggett
    Brigham and Women's Hospital, Boston, MA, USA.
  • Nicolas Duchateau
    Inria Asclepios research project, Sophia Antipolis, France.
  • Gemma Piella
    Information and Communication Technologies Department, Universitat Pompeu Fabra, Barcelona, Spain.
  • Constantine Butakoff
    Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona, Spain.
  • Anne Catherine Pouleur
    Division of Cardiology, Cliniques Saint-Luc UCL, Brussels, Belgium.
  • Dorit Knappe
    University Heart Center Hamburg, Hamburg, Germany.
  • Tor Biering-Sørensen
    Brigham and Women's Hospital, Boston, MA, USA.
  • Valentina Kutyifa
    University of Rochester, Rochester, NY, USA.
  • Arthur Moss
    University of Rochester, Rochester, NY, USA.
  • Kenneth Stein
    Boston Scientific, Minneapolis, MN, USA.
  • Scott D Solomon
    Brigham and Women's Hospital, Boston, MA, USA.
  • Bart Bijnens
    Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona, Spain.

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