A natural language processing algorithm to extract characteristics of subdural hematoma from head CT reports.

Journal: Emergency radiology
PMID: 30693414

Abstract

PURPOSE: Subdural hematoma (SDH) is the most common form of traumatic intracranial hemorrhage, and radiographic characteristics of SDH are predictive of complications and patient outcomes. We created a natural language processing (NLP) algorithm to extract structured data from cranial computed tomography (CT) scan reports for patients with SDH.

Authors

  • Peter Pruitt
    Department of Emergency Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. peter.pruitt@northwestern.edu.
  • Andrew Naidech
    Center for Healthcare Studies, Northwestern University Feinberg School of Medicine, 633 N St. Clair Street, Chicago, IL, 60622, USA.
  • Jonathan Van Ornam
    Harvard Affiliated Emergency Medicine Residency, Boston, MA, USA.
  • Pierre Borczuk
    Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
  • William Thompson
    Center for Health Information Partnerships, Northwestern University Feinberg School of Medicine, 625 N Michigan Ave., Chicago, IL, 60611, USA.

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