Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients.

Journal: Clinical kidney journal
Published Date: Jul 3, 2018

Abstract

BACKGROUND: We re-analyzed data from the Systolic Blood Pressure Intervention Trial (SPRINT) trial to identify features of systolic blood pressure (SBP) variability that portend poor cardiovascular outcomes using a nonlinear machine-learning algorithm.

Authors

  • Ronilda C Lacson
    Brigham and Women's Hospital, Boston MA, USA.
  • Bowen Baker
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Harini Suresh
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Katherine Andriole
    Brigham and Women's Hospital, Boston MA, USA.
  • Peter Szolovits
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Eduardo Lacson
    Dialysis Clinic, Inc., Nashville, TN, USA.

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