An active learning-enabled annotation system for clinical named entity recognition.

Journal: BMC medical informatics and decision making
Published Date: Jul 5, 2017

Abstract

BACKGROUND: Active learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models. However, very few studies have investigated AL in a real-life setting in medical domain.

Authors

  • Yukun Chen
    Department of Biomedical Informatics, Vanderbilt University, School of Medicine, Nashville, TN, USA.
  • Thomas A Lask
    Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
  • Qiaozhu Mei
    University of Michigan, Ann Arbor, MI.
  • Qingxia Chen
    Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
  • Sungrim Moon
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
  • Jingqi Wang
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Ky Nguyen
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Tolulola Dawodu
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Trevor Cohen
    University of Washington, Seattle, WA.
  • Joshua C Denny
    Vanderbilt University, Nashville, TN.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

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