Assessing the utility of deep neural networks in detecting superficial surgical site infections from free text electronic health record data.

Journal: Frontiers in digital health
Published Date: Jan 8, 2024

Abstract

BACKGROUND: High-quality outcomes data is crucial for continued surgical quality improvement. Outcomes are generally captured through structured administrative data or through manual curation of unstructured electronic health record (EHR) data. The aim of this study was to apply natural language processing (NLP) to chart notes in the EHR to accurately capture postoperative superficial surgical site infections (SSSIs).

Authors

  • Alexander Bonde
    Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
  • Stephan Lorenzen
    Aiomic, Copenhagen, Denmark.
  • Gustav Brixen
    Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Denmark.
  • Anders Troelsen
    Department of Orthopedics, Copenhagen University Hospital, Hvidovre, Denmark.
  • Martin Sillesen
    Department of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.

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