Exploiting Rules to Enhance Machine Learning in Extracting Information From Multi-Institutional Prostate Pathology Reports.

Journal: JCO clinical cancer informatics
Published Date: Oct 1, 2020

Abstract

PURPOSE: Literature on clinical note mining has highlighted the superiority of machine learning (ML) over hand-crafted rules. Nevertheless, most studies assume the availability of large training sets, which is rarely the case. For this reason, in the clinical setting, rules are still common. We suggest 2 methods to leverage the knowledge encoded in pre-existing rules to inform ML decisions and obtain high performance, even with scarce annotations.

Authors

  • Enrico Santus
    Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts, United States of America.
  • Tal Schuster
    From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Avon Comprehensive Breast Evaluation Center, 55 Fruit St, WAC 240, Boston, MA 02114-2698 (C.D.L., B.D., M.B.); and Massachusetts Institute of Technology, Cambridge, Mass (A.Y., T.S., K.S., R.B.).
  • Amir M Tahmasebi
    Philips Research North America, 2 Canal Park, 3rd Floor, Cambridge, MA, 02141, USA. amir.tahmasebi@philips.com.
  • Clara Li
    Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, USA.
  • Adam Yala
    Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, USA.
  • Conor R Lanahan
    Massachusetts General Hospital, Boston, MA.
  • Peter Prinsen
    Philips Research, Eindhoven, North Brabant, The Netherlands.
  • Scott F Thompson
    Philips Healthcare, Cambridge, MA.
  • Samuel Coons
    Philips Healthcare, Cambridge, MA.
  • Lance Mynderse
    Mayo Clinics, Rochester, MN.
  • Regina Barzilay
    Computer Science and Artificial Intelligence Laboratory , Massachusetts Institute of Technology , 77 Massachusetts Avenue , Cambridge , MA 02139 , USA . Email: regina@csail.mit.edu.
  • Kevin Hughes
    Division of Surgical Oncology, MGH, Boston, USA.

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