A Comparison of Five Algorithmic Methods and Machine Learning Pattern Recognition for Artifact Detection in Electronic Records of Five Different Vital Signs: A Retrospective Analysis.

Journal: Anesthesiology
PMID: 38466210

Abstract

BACKGROUND: Research on electronic health record physiologic data is common, invariably including artifacts. Traditionally, these artifacts have been handled using simple filter techniques. The authors hypothesized that different artifact detection algorithms, including machine learning, may be necessary to provide optimal performance for various vital signs and clinical contexts.

Authors

  • Mathias Maleczek
    Department of Anesthesiology, Intensive Care Medicine and Pain Medicine, and Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
  • Daniel Laxar
    Department of Anesthesiology, Intensive Care Medicine and Pain Medicine, and Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
  • Lorenz Kapral
    Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
  • Melanie Kuhrn
    Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
  • Yannic-Tomas Abulesz
    Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
  • Christoph Dibiasi
    Department of Anesthesiology, Intensive Care Medicine and Pain Medicine, and Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
  • Oliver Kimberger
    Department of Anesthesiology, Intensive Care Medicine and Pain Medicine, and Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.

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