Discrepancies in Stroke Distribution and Dataset Origin in Machine Learning for Stroke.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Published Date: Jul 1, 2021

Abstract

BACKGROUND: Machine learning algorithms depend on accurate and representative datasets for training in order to become valuable clinical tools that are widely generalizable to a varied population. We aim to conduct a review of machine learning uses in stroke literature to assess the geographic distribution of datasets and patient cohorts used to train these models and compare them to stroke distribution to evaluate for disparities.

Authors

  • Lohit Velagapudi
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
  • Nikolaos Mouchtouris
    Department of Neurosurgery, Thomas Jefferson University Hospitals, Philadelphia, Pennsylvania.
  • Michael P Baldassari
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
  • David Nauheim
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
  • Omaditya Khanna
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
  • Fadi Al Saiegh
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
  • Nabeel Herial
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
  • M Reid Gooch
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
  • Stavropoula Tjoumakaris
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
  • Robert H Rosenwasser
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
  • Pascal Jabbour
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA. Electronic address: pascal.jabbour@jefferson.edu.

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