Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.

Journal: Computers in biology and medicine
Published Date: Jun 1, 2019

Abstract

OBJECTIVE: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for sepsis detection and prediction, and compares its performance to existing methods.

Authors

  • Christopher Barton
    Department of Emergency Medicine, University of California San Francisco, San Francisco, California, USA.
  • Uli Chettipally
    Department of Emergency Medicine, University of California San Francisco, San Francisco, California, USA.
  • Yifan Zhou
    Department of Pharmacology, University of Oxford, Oxford, United Kingdom.
  • Zirui Jiang
    Dascena Inc., Oakland, CA, USA; Department of Nuclear Engineering, University of California Berkeley, Berkeley, CA, USA.
  • Anna Lynn-Palevsky
    Dascena Inc., Oakland, CA, USA.
  • Sidney Le
    Dascena Inc., Oakland, CA, USA.
  • Jacob Calvert
    Dascena Inc., Hayward, California, USA.
  • Ritankar Das
    Dascena, Inc, Hayward, California, USA.

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