Post-Cardiac arrest outcome prediction using machine learning: A systematic review and meta-analysis.

Journal: International journal of medical informatics
Published Date: Oct 28, 2024

Abstract

BACKGROUND: Early and reliable prognostication in post-cardiac arrest patients remains challenging, with various factors linked to return of spontaneous circulation (ROSC), survival, and neurological results. Machine learning and deep learning models show promise in improving these predictions. This systematic review and meta-analysis evaluates how effective these approaches are in predicting clinical outcomes at different time points using structured data.

Authors

  • Amirhosein Zobeiri
    Department of Mechatronics, School of Intelligent Systems, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran.
  • Alireza Rezaee
    Intelligent Mobile Robot Lab (IMRL), Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran. Electronic address: arrezaee@ut.ac.ir.
  • Farshid Hajati
    School of Information Technology and Engineering, MIT Sydney, Sydney, New South Wales, Australia.
  • Ahmadreza Argha
  • Hamid Alinejad-Rokny
    Systems Biology and Health Data Analytics Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, 2052 Sydney, Australia; School of Computer Science and Engineering, The University of New South Wales (UNSW Sydney), 2052 Sydney, Australia; Health Data Analytics Program Leader, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, Australia.

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