A machine learning based model for Out of Hospital cardiac arrest outcome classification and sensitivity analysis.

Journal: Resuscitation
PMID: 30885826

Abstract

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) affects nearly 400,000 people each year in the United States of which only 10% survive. Using data from the Cardiac Arrest Registry to Enhance Survival (CARES), and machine learning (ML) techniques, we developed a model of neurological outcome prediction for OHCA in Chicago, Illinois.

Authors

  • Samuel Harford
    Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, Illinois, United States.
  • Houshang Darabi
    Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Room 2055, ERF Building, 842 W Taylor Street, Chicago, IL 60607, USA.
  • Marina Del Rios
    Department of Emergency Medicine, University of Illinois at Chicago, Chicago, Illinois, United States. Electronic address: mdelrios@uic.edu.
  • Somshubra Majumdar
    Department of Computer Science, University of Illinois, Chicago, IL 60607, USA.
  • Fazle Karim
    Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, Illinois, United States.
  • Terry Vanden Hoek
    Department of Emergency Medicine, University of Illinois at Chicago, Chicago, Illinois, United States.
  • Kim Erwin
    Department of Population Health Sciences, University of Illinois at Chicago, Chicago, Illinois, United States.
  • Dennis P Watson
    Center of Dissemination and Implementation Science, University of Illinois at Chicago, Chicago, Illinois, United States.

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