Improving Prehospital Stroke Diagnosis Using Natural Language Processing of Paramedic Reports.

Journal: Stroke
PMID: 34162217

Abstract

BACKGROUND AND PURPOSE: Accurate prehospital diagnosis of stroke by emergency medical services (EMS) can increase treatments rates, mitigate disability, and reduce stroke deaths. We aimed to develop a model that utilizes natural language processing of EMS reports and machine learning to improve prehospital stroke identification.

Authors

  • Anoop Mayampurath
    Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.
  • Zahra Parnianpour
    Department of Neurology (Z.P., S.J.M., J.L.H., S.P.), University of Chicago, IL.
  • Christopher T Richards
    Department of Emergency Medicine, University of Cincinnati, OH (C.T.R.).
  • William J Meurer
    Department of Emergency Medicine, University of Michigan, Ann Arbor, IL (W.J.M.).
  • Jungwha Lee
    Institute for Public Health and Medicine, Chicago, IL, USA.
  • Bruce Ankenman
    Department of Industrial Engineering and Management Studies, Northwestern University (B.A., O.P.).
  • Ohad Perry
    Department of Industrial Engineering and Management Studies, Northwestern University (B.A., O.P.).
  • Scott J Mendelson
    Department of Neurology (Z.P., S.J.M., J.L.H., S.P.), University of Chicago, IL.
  • Jane L Holl
    Center for Healthcare Studies, Northwestern University, Chicago, Illinois.
  • Shyam Prabhakaran
    Center for Healthcare Studies, Northwestern University, Chicago, Illinois.

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