Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Published Date: May 15, 2019

Abstract

OBJECTIVE: The manual adjudication of disease classification is time-consuming, error-prone, and limits scaling to large datasets. In ischemic stroke (IS), subtype classification is critical for management and outcome prediction. This study sought to use natural language processing of electronic health records (EHR) combined with machine learning methods to automate IS subtyping.

Authors

  • Ravi Garg
    Center for Healthcare Studies, Northwestern University, Chicago, Illinois.
  • Elissa Oh
    Department of Neurology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois.
  • Andrew Naidech
    Center for Healthcare Studies, Northwestern University Feinberg School of Medicine, 633 N St. Clair Street, Chicago, IL, 60622, USA.
  • Konrad Kording
    Laura Prosser, PhD, PTR is a Assistant Professor of Pediatrics, the Perelman School of Medicine, University of Pennsylvania and a physical therapist, Children's Hospital of Philadelphia.
  • Shyam Prabhakaran
    Center for Healthcare Studies, Northwestern University, Chicago, Illinois.

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