Mining fall-related information in clinical notes: Comparison of rule-based and novel word embedding-based machine learning approaches.

Journal: Journal of biomedical informatics
Published Date: Jan 9, 2019

Abstract

BACKGROUND: Natural language processing (NLP) of health-related data is still an expertise demanding, and resource expensive process. We created a novel, open source rapid clinical text mining system called NimbleMiner. NimbleMiner combines several machine learning techniques (word embedding models and positive only labels learning) to facilitate the process in which a human rapidly performs text mining of clinical narratives, while being aided by the machine learning components.

Authors

  • Maxim Topaz
    Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Ludmila Murga
    Cheryl Spencer Department of Nursing, University of Haifa, Haifa, Israel.
  • Katherine M Gaddis
    School of Nursing, University of Pennsylvania, Philadelphia, PA, USA.
  • Margaret V McDonald
    The Visiting Nurse Service of New York, New York, NY, USA.
  • Ofrit Bar-Bachar
    Cheryl Spencer Department of Nursing, University of Haifa, Haifa, Israel.
  • Yoav Goldberg
    Department of Computer Science, Bar Ilan University, Tel Aviv, Israel.
  • Kathryn H Bowles
    Visiting Nurse Service of New York, NY, USA; School of Nursing, University of Pennsylvania, PA, USA.

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