Applying Machine Learning Across Sites: External Validation of a Surgical Site Infection Detection Algorithm.

Journal: Journal of the American College of Surgeons
Published Date: Jun 1, 2021

Abstract

BACKGROUND: Surgical complications have tremendous consequences and costs. Complication detection is important for quality improvement, but traditional manual chart review is burdensome. Automated mechanisms are needed to make this more efficient. To understand the generalizability of a machine learning algorithm between sites, automated surgical site infection (SSI) detection algorithms developed at one center were tested at another distinct center.

Authors

  • Ying Zhu
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Gyorgy J Simon
    Institute for Health Informatics; Department of Medicine, University of Minnesota, MN.
  • Elizabeth C Wick
    Division of Clinical Informatics and Digital Transformation, Department of Medicine, University of California San Francisco, San Francisco, CA; Division of General Surgery, Department of Surgery, University of California San Francisco, San Francisco, CA.
  • Yumiko Abe-Jones
    Departments of Surgery, University of California San Francisco, San Francisco, CA.
  • Nader Najafi
    Department of Medicine, University of California, San Francisco.
  • Adam Sheka
    Medicine, University of California San Francisco, San Francisco, CA.
  • Roshan Tourani
    Institute for Health Informatics, University of Minnesota, Twin Cities, Minneapolis, MN.
  • Steven J Skube
    Department of Surgery, University of Minnesota, Minneapolis, MN.
  • Zhen Hu
    Institute for Health Informatics.
  • Genevieve B Melton
    Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.

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