Use of Machine Learning to Identify Follow-Up Recommendations in Radiology Reports.

Journal: Journal of the American College of Radiology : JACR
Published Date: Mar 1, 2019

Abstract

PURPOSE: The aims of this study were to assess follow-up recommendations in radiology reports, develop and assess traditional machine learning (TML) and deep learning (DL) models in identifying follow-up, and benchmark them against a natural language processing (NLP) system.

Authors

  • Emmanuel Carrodeguas
    Harvard Medical School, Boston, Massachusetts; Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Brookline, Massachusetts. Electronic address: emmanuel_carrodeguas@hms.harvard.edu.
  • Ronilda Lacson
    Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont St, Boston, MA 02120.
  • Whitney Swanson
    Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Brookline, Massachusetts.
  • 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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