Machine Learning Model Drift: Predicting Diagnostic Imaging Follow-Up as a Case Example.

Journal: Journal of the American College of Radiology : JACR
Published Date: Aug 16, 2022

Abstract

OBJECTIVE: Address model drift in a machine learning (ML) model for predicting diagnostic imaging follow-up using data augmentation with more recent data versus retraining new predictive models.

Authors

  • Ronilda Lacson
    Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont St, Boston, MA 02120.
  • Mahsa Eskian
    Department of Radiology, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts.
  • Andro Licaros
    Department of Radiology, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts.
  • Neena Kapoor
    Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont St, Boston, MA 02120.
  • Ramin Khorasani
    Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont St, Boston, MA 02120.

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