Early prediction of shock in intensive care unit patients by machine learning using discrete electronic health record data.

Journal: Journal of critical care
Published Date: Apr 22, 2025

Abstract

PURPOSE: To use machine learning to predict new-onset shock for at-risk intensive care unit (ICU) patients based on discrete vital sign data from the electronic health record.

Authors

  • Jacob C Jentzer
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Shrinath Patel
  • Ognjen Gajic
    Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Vitaly Herasevich
    Department of Anesthesiology, Mayo Clinic, Rochester, MN, USA.
  • Francisco Lopez-Jimenez
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Dennis H Murphree
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA.
  • Parag C Patel
    Department of Cardiovascular Medicine, Mayo Clinic Florida, Jacksonville, Florida, USA.
  • Kianoush B Kashani
    Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA.

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