Large Vessel Occlusion Prediction in the Emergency Department with National Institutes of Health Stroke Scale Components: A Machine Learning Approach.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
PMID: 34403842

Abstract

OBJECTIVE: To determine the feasibility of using a machine learning algorithm to screen for large vessel occlusions (LVO) in the Emergency Department (ED).

Authors

  • Donglai Huo
    Department of Radiology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA. Donglai.Huo@ucdenver.edu.
  • Michelle Leppert
    Department of Neurology, University of Colorado School of Medicine, Aurora CO, US; Colorado Cardiovascular Outcomes Research (CCOR) Group, Denver CO, US.
  • Rebecca Pollard
    Department of Neurology, University of Colorado School of Medicine, Aurora CO, US.
  • Sharon N Poisson
    Department of Neurology, University of Colorado School of Medicine, Aurora CO, US.
  • Xiang Fang
    Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
  • David Rubinstein
    Department of Radiology, University of Colorado School of Medicine, Mail Stop C278, 12700 E 19th Ave., Aurora CO 80045, US.
  • Igor Malenky
    Department of Neurology, University of Colorado School of Medicine, Aurora CO, US.
  • Kelsey Eklund
    Department of Neurology, University of Colorado School of Medicine, Aurora CO, US.
  • Eric Nyberg
    University of Colorado Anschutz Medical Campus, Aurora, CO.

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