Using natural language processing and machine learning to identify breast cancer local recurrence.

Journal: BMC bioinformatics
Published Date: Dec 28, 2018

Abstract

BACKGROUND: Identifying local recurrences in breast cancer from patient data sets is important for clinical research and practice. Developing a model using natural language processing and machine learning to identify local recurrences in breast cancer patients can reduce the time-consuming work of a manual chart review.

Authors

  • Zexian Zeng
    Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
  • Sasa Espino
    Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Ankita Roy
    Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Xiaoyu Li
    Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Seema A Khan
    Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Susan E Clare
    Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Xia Jiang
    Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15213, United States of America.
  • Richard Neapolitan
    Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States of America.
  • Yuan Luo
    Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA.

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