Analysis of Stroke Detection during the COVID-19 Pandemic Using Natural Language Processing of Radiology Reports.

Journal: AJNR. American journal of neuroradiology
Published Date: Dec 17, 2020

Abstract

BACKGROUND AND PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to decreases in neuroimaging volume. Our aim was to quantify the change in acute or subacute ischemic strokes detected on CT or MR imaging during the pandemic using natural language processing of radiology reports.

Authors

  • M D Li
    From the Departments of Radiology (M.D.L., M.L., F.D., K.C., K.B., S.R., W.A.M., J.K.-C.) mdli@mgh.harvard.edu.
  • M Lang
  • F Deng
    From the Departments of Radiology (M.D.L., M.L., F.D., K.C., K.B., S.R., W.A.M., J.K.-C.).
  • K Chang
    Department of Radiology (K.C.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • K Buch
    From the Departments of Radiology (M.D.L., M.L., F.D., K.C., K.B., S.R., W.A.M., J.K.-C.).
  • S Rincon
    From the Departments of Radiology (M.D.L., M.L., F.D., K.C., K.B., S.R., W.A.M., J.K.-C.).
  • W A Mehan
    From the Departments of Radiology (M.D.L., M.L., F.D., K.C., K.B., S.R., W.A.M., J.K.-C.).
  • T M Leslie-Mazwi
    Neurology and Neurosurgery (T.M.L.-M.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • J Kalpathy-Cramer

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