Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

Journal: Critical care medicine
Published Date: Jul 1, 2016

Abstract

OBJECTIVE: The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability.

Authors

  • Lujie Chen
    Carnegie Mellon University Robotics Institute (Auton Lab), Pittsburgh, PA, USA.
  • Artur Dubrawski
    Auton Lab, School of Computer Science, Carnegie Mellon University Pittsburgh, PA, USA.
  • Donghan Wang
    Robotics Institute, Auton Lab, Carnegie Mellon University, Pittsburgh, PA, USA.
  • Madalina Fiterau
  • Mathieu Guillame-Bert
    Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA.
  • Eliezer Bose
    Department of Acute and Tertiary Care, University of Pittsburgh Schools of Nursing, 336 Victoria Hall; 3500 Victoria St., Pittsburgh, PA, 15261, USA.
  • Ata M Kaynar
  • David J Wallace
  • Jane Guttendorf
  • Gilles Clermont
    Cardiopulmonary Research Laboratory, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA.
  • Michael R Pinsky
    Cardiopulmonary Research Laboratory, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA.
  • Marilyn Hravnak
    Department of Acute and Tertiary Care, University of Pittsburgh Schools of Nursing, 336 Victoria Hall; 3500 Victoria St., Pittsburgh, PA, 15261, USA. mhra@pitt.edu.

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