Using Optimal Feature Selection and Continuous Learning to Implement Efficient Model Arrays for Predicting Daily Clinical Radiology Workload.

Journal: Academic radiology
Published Date: Mar 25, 2025

Abstract

RATIONALE AND OBJECTIVE: Clinical workload can fluctuate daily in radiology practice. We sought to design, validate, and implement an efficient and sustainable machine learning model to forecast daily clinical image interpretation workload.

Authors

  • Leslie K Lee
    Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Melissa Viator
    Mass General Brigham Hospital, Boston, Massachusetts.
  • Catherine S Giess
    Mass General Brigham Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.
  • Michael Gee
    Mass General Brigham Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.
  • Ray Huang
    MIT Economics, Blueprint Labs, Cambridge, MA, 02142, US.
  • Fionnuala McPeake
    Mass General Brigham Hospital, Boston, Massachusetts.
  • Oleg S Pianykh
    Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts. Electronic address: opianykh@mgh.harvard.edu.

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