Natural Language Processing for the Identification of Surgical Site Infections in Orthopaedics.

Journal: The Journal of bone and joint surgery. American volume
Published Date: Dec 18, 2019

Abstract

BACKGROUND: The identification of surgical site infections for infection surveillance in hospitals depends on the manual abstraction of medical records and, for research purposes, depends mainly on the use of administrative or claims data. The objective of this study was to determine whether automating the abstraction process with natural language processing (NLP)-based models that analyze the free-text notes of the medical record can identify surgical site infections with predictive abilities that match the manual abstraction process and that surpass surgical site infection identification from administrative data.

Authors

  • Caroline P Thirukumaran
    University of Rochester, Rochester, New York.
  • Anis Zaman
    University of Rochester, Rochester, New York.
  • Paul T Rubery
    University of Rochester, Rochester, New York.
  • Casey Calabria
    University of Rochester, Rochester, New York.
  • Yue Li
    School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, China.
  • Benjamin F Ricciardi
    University of Rochester, Rochester, New York.
  • Wajeeh R Bakhsh
    Center for Cervical Spine, Washington University Orthopedics, Washington University in St. Louis, Saint Louis, Missouri, United States.
  • Henry Kautz
    University of Rochester, Rochester, New York.

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