Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention.

Journal: JACC. Cardiovascular interventions
Published Date: Jul 22, 2019

Abstract

OBJECTIVES: This study sought to determine whether machine learning can be used to better identify patients at risk for death or congestive heart failure (CHF) rehospitalization after percutaneous coronary intervention (PCI).

Authors

  • Chad J Zack
    Heart and Vascular Institute, Penn State Hershey Medical Center, Hershey, Pennsylvania.
  • Conor Senecal
    Department of Cardiovascular Diseases, Mayo Clinic College of Medicine, Rochester, Minnesota.
  • Yaron Kinar
    Medial Research, Kfar Malal, Israel.
  • Yaakov Metzger
    Medial Research, Kfar Malal, Israel.
  • Yoav Bar-Sinai
    Medial Research, Kfar Malal, Israel.
  • R Jay Widmer
    Department of Cardiovascular Diseases, Mayo Clinic College of Medicine, Rochester, Minnesota.
  • Ryan Lennon
    Division of Biostatistics, Mayo Clinic College of Medicine, Rochester, Minnesota.
  • Mandeep Singh
    Department of Cardiovascular Diseases, Mayo Clinic College of Medicine, Rochester, Minnesota.
  • Malcolm R Bell
    Department of Cardiovascular Diseases, Mayo Clinic College of Medicine, Rochester, Minnesota.
  • Amir Lerman
    Department of Cardiovascular Diseases, Mayo Clinic College of Medicine, Rochester, Minnesota.
  • Rajiv Gulati
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.

Keywords

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