Risk of bias assessment of post-stroke mortality machine learning predictive models: Systematic review.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
PMID: 40089217

Abstract

BACKGROUND: Stroke is a major cause of mortality and permanent disability worldwide. Precise prediction of post-stroke mortality is essential for guiding treatment decisions and rehabilitation planning. The ability of Machine learning models to process large amounts of data, offer a promising alternative for improving mortality prediction in stroke patients. In this review, we aim to evaluate the risk of bias in different machine learning models used for predicting post-stroke mortality.

Authors

  • Nicole Maria Radley
    Imperial College London, United Kingdom.
  • Ian Soh
    St George's, University of London, United Kingdom.
  • Abdelrahman M Saad
    Faculty of Medicine, Alexandria University, Alexandria, Egypt.
  • Milindu Wickramarachchi
    School of Clinical Medicine, University of Cambridge, United Kingdom.
  • Amelia Dawson
    Kavanagh, Chesterfield Royal Hospital Foundation Trust, United Kingdom.
  • Jeremy Ng Chieng Hin
    Wirral University Teaching Hospital NHS Foundation Trust, United Kingdom.
  • Asad Ali
    Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
  • Abhrajit Giri
    Nottingham University Hospitals NHS Trust, United Kingdom.
  • Alicia Kwan
    Nottingham University Hospitals NHS Trust, United Kingdom.
  • Osama Elzankaly
    Alexandria University of Medicine, Egypt.
  • Mariam Tarek Desouki
    Alexandria University of Medicine, Egypt.
  • Mohamed S Jabal
    Department of Radiology, Mayo Clinic, Rochester, MN 55905, United States.
  • Abdelrahman M Hamouda
    Department of Neurological Surgery, Mayo Clinic, 200 1st SW Rochester, Rochester, MN 55905, United States. Electronic address: Hamouda.abdelrahman@mayo.edu.
  • Sherief Ghozy
    Department of Neuroradiology, Mayo Clinic, Rochester, Minnesota, USA.
  • David F Kallmes
    Department of Radiology, Mayo Clinic, Rochester, MN, USA.

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