Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning Algorithm.

Journal: Annals of emergency medicine
Published Date: Jan 15, 2021

Abstract

STUDY OBJECTIVE: Machine-learning algorithms allow improved prediction of sepsis syndromes in the emergency department (ED), using data from electronic medical records. Transfer learning, a new subfield of machine learning, allows generalizability of an algorithm across clinical sites. We aim to validate the Artificial Intelligence Sepsis Expert for the prediction of delayed septic shock in a cohort of patients treated in the ED and demonstrate the feasibility of transfer learning to improve external validity at a second site.

Authors

  • Gabriel Wardi
    Emergency Medicine, University of California San Diego, La Jolla, California, USA.
  • Morgan Carlile
    Department of Emergency Medicine, University of California, San Diego, La Jolla, CA.
  • Andre Holder
    Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, Emory University School of Medicine, Atlanta, GA.
  • Supreeth Shashikumar
    Department of Biomedical Informatics, University of California-San Diego, San Diego, CA.
  • Stephen R Hayden
    Department of Emergency Medicine, University of California-San Diego, San Diego, CA.
  • Shamim Nemati
    Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, USA.

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