Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.

Journal: Circulation. Cardiovascular quality and outcomes
Published Date: Sep 5, 2019

Abstract

BACKGROUND: The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scale data could substantially expand the clinical inferences derived from the ECG while at the same time preserving interpretability for medical decision-making.

Authors

  • Geoffrey H Tison
    Department of Medicine (G.H.T., M.H.L., E.F., M.A.A., C.J., K.E.F., R.C.D.).
  • Jeffrey Zhang
    Cardiovascular Research Institute (J.Z., R.C.D.).
  • Francesca N Delling
    Division of Cardiology, Department of Medicine (G.H.T., F.N.D., R.C.D.), University of California, San Francisco.
  • Rahul C Deo
    From the Division of Cardiology, Department of Medicine; Cardiovascular Research Institute; Institute for Human Genetics; and Institute for Computational Health Sciences, University of California San Francisco, and California Institute for Quantitative Biosciences (R.C.D.); and VA Health Services Research and Development Center for Clinical Management Research, VA Ann Arbor Healthcare System, MI; Michigan Center for Health Analytics and Medical Prediction (M-CHAMP), Department of Internal Medicine, University of Michigan Medical School, Ann Arbor (B.K.N.). rahul.deo@ucsf.edu.

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