Machine Learning of Physiologic Waveforms and Electronic Health Record Data: A Large Perioperative Data Set of High-Fidelity Physiologic Waveforms.

Journal: Critical care clinics
PMID: 37704333

Abstract

Perioperative morbidity and mortality are significantly associated with both static and dynamic perioperative factors. The studies investigating static perioperative factors have been reported; however, there are a limited number of previous studies and data sets analyzing dynamic perioperative factors, including physiologic waveforms, despite its clinical importance. To fill the gap, the authors introduce a novel large size perioperative data set: Machine Learning Of physiologic waveforms and electronic health Record Data (MLORD) data set. They also provide a concise tutorial on machine learning to illustrate predictive models trained on complex and diverse structures in the MLORD data set.

Authors

  • Sungsoo Kim
    Bio Convergence Research Institute, Bertis Inc., Heungdeok 1-ro, Giheung-gu, Yongin-si, 16954 Gyeonggi-do, Republic of Korea.
  • Sohee Kwon
    Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.
  • Akos Rudas
    Department of Computational Medicine, UCLA, Los Angeles, California, United States of America.
  • Ravi Pal
    Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.
  • Mia K Markey
    Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA; Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: mia.markey@utexas.edu.
  • Alan C Bovik
  • Maxime Cannesson
    Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.

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