An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

Journal: British journal of anaesthesia
Published Date: Dec 1, 2019

Abstract

BACKGROUND: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level or utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart.

Authors

  • Brian L Hill
    Department of Computer Science, University of California, Los Angeles, CA, USA.
  • Robert Brown
    Department of Computer Science, University of California, Los Angeles, CA, USA.
  • Eilon Gabel
  • Nadav Rakocz
    Department of Computer Science, University of California, Los Angeles, CA, USA.
  • Christine Lee
    Department of Psychiatry and Behavioral Sciences, University of Washington, USA.
  • Maxime Cannesson
    Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
  • Pierre Baldi
    Department of Computer Science, Department of Biological Chemistry, University of California-Irvine, Irvine, CA 92697, USA.
  • Loes Olde Loohuis
    Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behaviour, University of California, Los Angeles, CA, USA.
  • Ruth Johnson
    Department of Computer Science, University of California, Los Angeles, CA, USA.
  • Brandon Jew
    Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA.
  • Uri Maoz
    Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, USA; Computational Neuroscience, Health and Behavioral Sciences and Brain Institute, Chapman University, Orange, CA 92866, USA; Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA 92866, USA; Department of Anesthesiology, School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.
  • Aman Mahajan
    Department of Anaesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
  • Sriram Sankararaman
    Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
  • Ira Hofer
  • Eran Halperin
    Departments of Computer Science and Biomathmatics, UCLA Henry Samueli School of Engineering and Applied Science.

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