Prediction of ROSC After Cardiac Arrest Using Machine Learning

Stud Health Technol Inform. 2020 Jun 16:270:1357-1358. doi: 10.3233/SHTI200440.

Abstract

Out-of-hospital cardiac arrest (OHCA) is an important public health problem, with very low survival rate. In treating OHCA patients, the return of spontaneous circulation (ROSC) represents the success of early resuscitation efforts. In this study, we developed a machine learning model to predict ROSC and compared it with the ROSC after cardiac arrest (RACA) score. Results demonstrated the usefulness of machine learning in deriving predictive models.

Keywords: Out-of-hospital cardiac arrest; ROSC; machine learning; random forest.

MeSH terms

  • Cardiopulmonary Resuscitation
  • Emergency Medical Services*
  • Humans
  • Machine Learning
  • Out-of-Hospital Cardiac Arrest*
  • Physiological Phenomena*
  • Retrospective Studies
  • Survival Rate