Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis.

Journal: Anesthesiology
PMID: 29894315

Abstract

WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: With appropriate algorithms, computers can learn to detect patterns and associations in large data sets. The authors' goal was to apply machine learning to arterial pressure waveforms and create an algorithm to predict hypotension. The algorithm detects early alteration in waveforms that can herald the weakening of cardiovascular compensatory mechanisms affecting preload, afterload, and contractility.

Authors

  • Feras Hatib
    From Edwards Lifesciences Critical Care, Irvine, California (F.H., Z.J., S.B., C.L., J.S.) the Department of Anesthesiology and Perioperative Care, School of Medicine (C.L., J.R., M.C.) Department of Computer Sciences (C.L.) Department of Biomedical Engineering (C.L., M.C.), University of California, Irving, California the Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California (K.S., M.C.).
  • Zhongping Jian
  • Sai Buddi
  • Christine Lee
    Department of Psychiatry and Behavioral Sciences, University of Washington, USA.
  • Jos Settels
  • Karen Sibert
  • Joseph Rinehart
  • 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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