An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records.

Journal: BMC medical informatics and decision making
Published Date: Dec 5, 2019

Abstract

BACKGROUND: Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number of professional and uncommon clinical terms in Chinese electronic medical records (EMRs), there are still many difficulties in clinical named entity recognition of Chinese EMRs. It is of great importance to eliminate semantic interference and improve the ability of autonomous learning of internal features of the model under the small training corpus.

Authors

  • Luqi Li
    Institute of Medical Information, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China.
  • Jie Zhao
    Department of Liver & Gallbladder Surgery, The First People's Hospital, Shangqiu, Henan, China.
  • Li Hou
    Institute of Medical Information & Library, Chinese Academy of Medical Sciences, Beijing, China.
  • Yunkai Zhai
    Center of Telemedicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • Jinming Shi
    National Engineering Laboratory for Internet Medical Systems and Applications, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Fangfang Cui
    National Engineering Laboratory for Internet Medical Systems and Applications, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

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