• Sungsoo Kim, Sohee Kwon, Akos Rudas, Ravi Pal, Mia K Markey, Alan C Bovik, and Maxime Cannesson.
• Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Electrical & Computer Engineering, The University of Texas at Austin, Austin, TX, USA.
• Crit Care Clin. 2023 Oct 1; 39 (4): 675687675-687.

AbstractPerioperative 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.Copyright © 2023 Elsevier Inc. All rights reserved.

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