Evaluation of Automated Public De-Identification Tools on a Corpus of Radiology Reports.

Journal: Radiology. Artificial intelligence
Published Date: Oct 14, 2020

Abstract

PURPOSE: To evaluate publicly available de-identification tools on a large corpus of narrative-text radiology reports.

Authors

  • Jackson M Steinkamp
    Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.M.S., T.P., J.A., C.E.K., T.S.C.); and Boston University School of Medicine, Boston, Mass (J.M.S.).
  • Taylor Pomeranz
    Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.M.S., T.P., J.A., C.E.K., T.S.C.); and Boston University School of Medicine, Boston, Mass (J.M.S.).
  • Jason Adleberg
    Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.M.S., T.P., J.A., C.E.K., T.S.C.); and Boston University School of Medicine, Boston, Mass (J.M.S.).
  • Charles E Kahn
    Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA.
  • Tessa S Cook
    Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.M.S., T.P., J.A., C.E.K., T.S.C.); and Boston University School of Medicine, Boston, Mass (J.M.S.).

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