Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Sep 18, 2021

Abstract

OBJECTIVE: : Developing clinical natural language processing systems often requires access to many clinical documents, which are not widely available to the public due to privacy and security concerns. To address this challenge, we propose to develop methods to generate synthetic clinical notes and evaluate their utility in real clinical natural language processing tasks.

Authors

  • Jianfu Li
    Mayo Clinic.
  • Yujia Zhou
    Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT 06510, United States.
  • Xiaoqian Jiang
    School of Biomedical Informatics, University of Texas Health, Science Center at Houston, Houston, TX, USA.
  • Karthik Natarajan
    Department of Biomedical Informatics, Columbia University, New York, NY, USA.
  • Serguei Vs Pakhomov
    College of Pharmacy, University of Minnesota, Minneapolis, Minnesota, USA.
  • Hongfang Liu
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

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