A machine learning-based clinical decision support algorithm for reducing unnecessary coronary angiograms.

Journal: Cardiovascular digital health journal
Published Date: Dec 24, 2021

Abstract

BACKGROUND: Conventional clinical risk scores and diagnostic algorithms are proving to be suboptimal in the prediction of obstructive coronary artery disease, contributing to the low diagnostic yield of invasive angiography. Machine learning could help better predict which patients would benefit from invasive angiography vs other noninvasive diagnostic modalities.

Authors

  • J D Schwalm
    Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Canada.
  • Shuang Di
    Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, Canada.
  • Tej Sheth
    Division of Cardiology, Hamilton General Hospital, Hamilton Health Sciences, McMaster University, Hamilton, ON, Canada.
  • Madhu K Natarajan
    Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Canada.
  • Erin O'Brien
    Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Canada.
  • Tara McCready
    Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Canada.
  • Jeremy Petch
    Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Canada.

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