Machine learning in the integration of simple variables for identifying patients with myocardial ischemia.

Journal: Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
PMID: 29790017

Abstract

BACKGROUND: A significant number of variables are obtained when characterizing patients suspected with myocardial ischemia or at risk of MACE. Guidelines typically use a handful of them to support further workup or therapeutic decisions. However, it is likely that the numerous available predictors maintain intrinsic complex interrelations. Machine learning (ML) offers the possibility to elucidate complex patterns within data to optimize individual patient classification. We evaluated the feasibility and performance of ML in utilizing simple accessible clinical and functional variables for the identification of patients with ischemia or an elevated risk of MACE as determined through quantitative PET myocardial perfusion reserve (MPR).

Authors

  • Luis Eduardo Juarez-Orozco
    Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland. l.e.juarez.orozco@gmail.com.
  • Remco J J Knol
    Cardiac Imaging Division Alkmaar, Department of Nuclear Medicine, Northwest Clinics, Alkmaar, The Netherlands.
  • Carlos A Sanchez-Catasus
    Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Octavio Martinez-Manzanera
    Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Friso M van der Zant
    Cardiac Imaging Division Alkmaar, Department of Nuclear Medicine, Northwest Clinics, Alkmaar, The Netherlands.
  • Juhani Knuuti
    Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland.

Keywords

External Resources

Popular Topics
Recent Journals