The performance of machine learning for predicting the recurrent stroke: a systematic review and meta-analysis on 24,350 patients.

Journal: Acta neurologica Belgica
Published Date: Nov 7, 2024

Abstract

BACKGROUND: Stroke is a leading cause of death and disability worldwide. Approximately one-third of patients with stroke experienced a second stroke. This study investigates the predictive value of machine learning (ML) algorithms for recurrent stroke.

Authors

  • Mohammad Amin Habibi
    Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Farhang Rashidi
    School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
  • Ehsan Mehrtabar
    Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.
  • Mohammad Reza Arshadi
    Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.
  • Mohammad Sadegh Fallahi
    Department of Neurosurgery, Tehran University of Medical Sciences, Tehran, Iran.
  • Nikan Amirkhani
    School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
  • Bardia Hajikarimloo
    Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Milad Shafizadeh
    Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Shahram Majidi
    Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA.
  • Adam A Dmytriw
    Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

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