Characterizing advanced heart failure risk and hemodynamic phenotypes using interpretable machine learning.

Journal: American heart journal
PMID: 38336159

Abstract

BACKGROUND: Although previous risk models exist for advanced heart failure with reduced ejection fraction (HFrEF), few integrate invasive hemodynamics or support missing data. This study developed and validated a heart failure (HF) hemodynamic risk and phenotyping score for HFrEF, using Machine Learning (ML).

Authors

  • Josephine Lamp
    Department of Computer Science, University of Virginia, Charlottesville, VA. Electronic address: jl4rj@virginia.edu.
  • Yuxin Wu
  • Steven Lamp
    Department of Computer Science, University of Virginia, Charlottesville, VA.
  • Prince Afriyie
    Department of Statistics, University of Virginia, Charlottesville, VA.
  • Nicholas Ashur
    Department of Cardiovascular Medicine, University of Virginia, Charlottesville, VA.
  • Kenneth Bilchick
    Department of Cardiovascular Medicine, University of Virginia, Charlottesville, VA.
  • Khadijah Breathett
    Division of Cardiovascular Medicine, Department of Medicine, University of Arizona, Tucson Arizona, USA.
  • Younghoon Kwon
    Department of Medicine, University of Washington, Seattle, WA, USA.
  • Song Li
    Department of Crop and Soil Environmental Sciences, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
  • Nishaki Mehta
    Department of Cardiology, William Beaumont Oakland University School of Medicine, Royal Oak, MI.
  • Edward Rojas Pena
    Department of Cardiovascular Medicine, University of Virginia, Charlottesville, VA.
  • Lu Feng
    Department of Medical Oncology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061,China.
  • Sula Mazimba
    Department of Cardiovascular Medicine, University of Virginia, Charlottesville, VA; Transplant Institute, AdventHealth, Orlando, FL.

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