Labeling Noncontrast Head CT Reports for Common Findings Using Natural Language Processing.

Journal: AJNR. American journal of neuroradiology
Published Date: Apr 28, 2022

Abstract

BACKGROUND AND PURPOSE: Prioritizing reading of noncontrast head CT examinations through an automated triage system may improve time to care for patients with acute neuroradiologic findings. We present a natural language-processing approach for labeling findings in noncontrast head CT reports, which permits creation of a large, labeled dataset of head CT images for development of emergent-finding detection and reading-prioritization algorithms.

Authors

  • M Iorga
    From the Departments of Radiology (M.I., M.D., T.B.P., V.B.H.) michael.iorga@northwestern.edu.
  • M Drakopoulos
    From the Departments of Radiology (M.I., M.D., T.B.P., V.B.H.).
  • A M Naidech
    Neurology (A.M.N.), Northwestern University Feinberg School of Medicine, Chicago, Illinois.
  • A K Katsaggelos
    Departments of Biomedical Engineering (M.I., A.K.K., T.B.P.).
  • T B Parrish
    From the Departments of Radiology (M.I., M.D., T.B.P., V.B.H.).
  • V B Hill
    From the Departments of Radiology (M.I., M.D., T.B.P., V.B.H.).

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