A machine learning framework to adjust for learning effects in medical device safety evaluation.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 39471493

Abstract

OBJECTIVES: Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects.

Authors

  • Jejo D Koola
    UC Health Department of Biomedical Informatics, University of California San Diego, 9500 Gilman Dr. MC 0728, La Jolla, San Diego, CA, 92093-0728, USA.
  • Karthik Ramesh
    School of Medicine, University of California San Diego, San Diego, CA 92093, United States.
  • Jialin Mao
    Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States.
  • Minyoung Ahn
    Jacobs School of Engineering, University of California San Diego, San Diego, CA 92093, United States.
  • Sharon E Davis
    Vanderbilt University School of Medicine, Nashville, TN.
  • Usha Govindarajulu
    Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States.
  • Amy M Perkins
    Deparment of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.
  • Dax Westerman
    Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Ave, Suite 1475, Nashville, TN, 37203, USA.
  • Henry Ssemaganda
    Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, 41 Mall Road, Burlington, MA, 01803, USA.
  • Theodore Speroff
    Departments of Medicine and Biostatistics, Vanderbilt University Medical Center, 1313 21St Avenue South, Oxford House, Room 209, Nashville, TN, 37232, USA.
  • Lucila Ohno-Machado
    University of California San Diego, La Jolla, CA.
  • Craig R Ramsay
    Health Services Research Unit, University of Aberdeen, Health Sciences Building, Foresterhill, 3rd Floor, Aberdeen, AB25 2ZD, UK.
  • Art Sedrakyan
    Populational Health Sciences, Weill Cornell Medicine, New York City, New York.
  • Frederic S Resnic
    Division of Cardiovascular Medicine and Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, Tufts University School of Medicine, 41 Burlington Mall Road, Burlington, MA, 01805, USA.
  • Michael E Matheny
    Vanderbilt University School of Medicine, Nashville, TN.

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