A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.

Journal: Critical care medicine
Published Date: Nov 1, 2019

Abstract

OBJECTIVES: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.

Authors

  • Heather M Giannini
    Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA.
  • Jennifer C Ginestra
    Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA.
  • Corey Chivers
    Penn Medicine, University of Pennsylvania, Philadelphia.
  • Michael Draugelis
    University of Pennsylvania Health System, Philadelphia, PA.
  • Asaf Hanish
    University of Pennsylvania Health System, Philadelphia, PA.
  • William D Schweickert
    University of Pennsylvania Health System, Philadelphia, PA.
  • Barry D Fuchs
    1 Pulmonary, Allergy, and Critical Care Division, Hospital of the University of Pennsylvania.
  • Laurie Meadows
    Department of Nursing, Hospital of the University of Pennsylvania, Philadelphia, PA.
  • Michael Lynch
    Department of Nursing, Hospital of the University of Pennsylvania, Philadelphia, PA.
  • Patrick J Donnelly
    Department of Electrical Engineering and Computer Science, Science, Oregon State University Cascades Campus, Bend, OR, United States.
  • Kimberly Pavan
    Penn Presbyterian Medical Center, Philadelphia, PA.
  • Neil O Fishman
    University of Pennsylvania Health System, Philadelphia, PA.
  • C William Hanson
    University of Pennsylvania Health System, Philadelphia, PA.
  • Craig A Umscheid
    University of Pennsylvania Health System, Philadelphia, PA.

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