A machine-learning approach to predicting hypotensive events in ICU settings.

Journal: Computers in biology and medicine
PMID: 32174328

Abstract

BACKGROUND: Predicting hypotension well in advance provides physicians with enough time to respond with proper therapeutic measures. However, the real-time prediction of hypotension with high positive predictive value (PPV) is a challenge. This is due to the dynamic changes in patients' physiological status following drug administration, which limits the quantity of useful data available for the algorithm.

Authors

  • Mina Chookhachizadeh Moghadam
    Department of Electrical Engineering and Computer Science, University of California Irvine, Irvine, CA, 92697, USA. Electronic address: mchmghdm@uci.edu.
  • Ehsan Masoumi Khalil Abad
    Department of Electrical Engineering and Computer Science, University of California Irvine, Irvine, CA, 92697, USA.
  • Nader Bagherzadeh
    Department of Electrical Engineering and Computer Science, University of California Irvine, Irvine, CA, 92697, USA.
  • Davinder Ramsingh
    Department of Anesthesiology, Loma Linda University Medical Center, 11234 Anderson St, Loma Linda, CA, 92354, USA.
  • Guann-Pyng Li
    Department of Electrical Engineering and Computer Science, University of California Irvine, Irvine, CA, 92697, USA.
  • Zeev N Kain
    Department of Anesthesiology and Preoperative Care, School of Medicine, University of California Irvine, Irvine, CA, 92697, USA.

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