Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data.

Journal: BMC medical informatics and decision making
Published Date: Oct 2, 2020

Abstract

BACKGROUND: With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only 'known' risk factors in predicting incident myocardial infarction (MI) from harmonized EHR data.

Authors

  • Divneet Mandair
    Division of Internal Medicine, University of Colorado School of Medicine, Aurora, CO, USA.
  • Premanand Tiwari
    Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora.
  • Steven Simon
    Division of Cardiology and Cardiac Electrophysiology, University of Colorado School of Medicine, 12631 E. 17th Avenue, Mail Stop B130, Aurora, CO, 80045, USA.
  • Kathryn L Colborn
    University of Colorado Anschutz Medical Campus, Colorado School of Public Health, Department of Biostatistics and Informatics, Aurora, CO, USA. Electronic address: kathryn.colborn@CUAnschutz.edu.
  • Michael A Rosenberg
    Division of Cardiology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America.

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