Accelerating Surgical Site Infection Abstraction With a Semi-automated Machine-learning Approach.

Journal: Annals of surgery
Published Date: Jul 1, 2022

Abstract

OBJECTIVE: To demonstrate that a semi-automated approach to health data abstraction provides significant efficiencies and high accuracy.

Authors

  • Steven J Skube
    Department of Surgery, University of Minnesota, Minneapolis, MN.
  • Zhen Hu
    Institute for Health Informatics.
  • 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.
  • Elliot G Arsoniadis
    Institute for Health Informatics; Department of Surgery.
  • Clifford Y Ko
    Department of Surgery, University of California Los Angeles, Los Angeles, California.
  • Genevieve B Melton
    Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.

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