Can machine learning improve patient selection for cardiac resynchronization therapy?

Journal: PloS one
Published Date: Oct 3, 2019

Abstract

RATIONALE: Multiple clinical trials support the effectiveness of cardiac resynchronization therapy (CRT); however, optimal patient selection remains challenging due to substantial treatment heterogeneity among patients who meet the clinical practice guidelines.

Authors

  • Szu-Yeu Hu
    Department of Radiology, Masachusetts General Hospital, Boston, Massachusetts, United States of America.
  • Enrico Santus
    Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts, United States of America.
  • Alexander W Forsyth
    Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts.
  • Devvrat Malhotra
    Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts, United States of America.
  • Josh Haimson
    Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts, United States of America.
  • Neal A Chatterjee
    Division of Cardiology, Department of Medicine, University of Washington, Seattle, Washington, United States of America.
  • Daniel B Kramer
    Richard A. and Susan F. Smith Center for Outcomes Research, Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America.
  • Regina Barzilay
    Computer Science and Artificial Intelligence Laboratory , Massachusetts Institute of Technology , 77 Massachusetts Avenue , Cambridge , MA 02139 , USA . Email: regina@csail.mit.edu.
  • James A Tulsky
    Harvard Medical School, Boston, MA.
  • Charlotta Lindvall
    Harvard Medical School, Boston, MA.

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