Preserving privacy in big data research: the role of federated learning in spine surgery.

Journal: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
Published Date: Feb 25, 2024

Abstract

PURPOSE: Integrating machine learning models into electronic medical record systems can greatly enhance decision-making, patient outcomes, and value-based care in healthcare systems. Challenges related to data accessibility, privacy, and sharing can impede the development and deployment of effective predictive models in spine surgery. Federated learning (FL) offers a decentralized approach to machine learning that allows local model training while preserving data privacy, making it well-suited for healthcare settings. Our objective was to describe federated learning solutions for enhanced predictive modeling in spine surgery.

Authors

  • Hania Shahzad
    Department of Orthopaedics, UC Davis Medical Center, Sacramento, CA, USA.
  • Cole Veliky
    Ohio State College of Medicine, The Ohio State University, Columbus, OH, USA.
  • Hai Le
    UC Davis Medical Center, Sacramento, CA, USA.
  • Sheeraz Qureshi
    Hospital for Special Surgery, New York, NY.
  • Frank M Phillips
    Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA.
  • Yashar Javidan
    UC Davis Medical Center, Sacramento, CA, USA.
  • Safdar N Khan
    Department of Orthopaedics, UC Davis Medical Center, Sacramento, CA, USA. safkhan@ucdavis.edu.

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