Clinical concept extraction: A methodology review.

Journal: Journal of biomedical informatics
Published Date: Aug 6, 2020

Abstract

BACKGROUND: Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement.

Authors

  • Sunyang Fu
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
  • David Chen
    Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
  • Huan He
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
  • Sijia Liu
    These authors contributed equally to this study and Dr. Li is now working at IBM; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA.
  • Sungrim Moon
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
  • Kevin J Peterson
    Center for Digital Health, Mayo Clinic, Rochester, MN.
  • Feichen Shen
    Department of Health Sciences Research, Rochester MN.
  • Liwei Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Yanshan Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Andrew Wen
    Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.
  • Yiqing Zhao
    Department of Health Informatics and Administration, Center for Biomedical Data and Language Processing, University of Wisconsin-Milwaukee, 2025 E Newport Ave, NWQ-B Room 6469, Milwaukee, WI, 53211, USA.
  • Sunghwan Sohn
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
  • Hongfang Liu
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.

Keywords

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