Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.

Journal: Journal of the American Heart Association
PMID: 29650709

Abstract

BACKGROUND: Whereas heart failure (HF) is a complex clinical syndrome, conventional approaches to its management have treated it as a singular disease, leading to inadequate patient care and inefficient clinical trials. We hypothesized that applying advanced analytics to a large cohort of HF patients would improve prognostication of outcomes, identify distinct patient phenotypes, and detect heterogeneity in treatment response.

Authors

  • Tariq Ahmad
    Section of Cardiovascular Medicine and Center for Outcomes Research, Yale University School of Medicine New Haven, CT tariq.ahmad@yale.edu.
  • Lars H Lund
    Department of Cardiology, Heart, Vascular and Neuro Theme, Karolinska University Hospital, Stockholm, Sweden.
  • Pooja Rao
    Qure.ai, Mumbai, India.
  • Rohit Ghosh
    Qure.ai, Mumbai, India.
  • Prashant Warier
    Qure.ai, Mumbai, India.
  • Benjamin Vaccaro
    Section of Cardiovascular Medicine and Center for Outcomes Research, Yale University School of Medicine New Haven, CT.
  • Ulf Dahlström
    Department of Medicine and Health Sciences, Linköping University, Linköping, Sweden.
  • Christopher M O'Connor
    Inova Schar Heart and Vascular, Falls Church, Virginia, USA; Duke Clinical Research Institute, Durham, North Carolina, USA.
  • G Michael Felker
    Duke Clinical Research Institute, Duke University, Durham, NC.
  • Nihar R Desai
    Section of Cardiovascular Medicine and Center for Outcomes Research, Yale University School of Medicine New Haven, CT.

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