SBLC: a hybrid model for disease named entity recognition based on semantic bidirectional LSTMs and conditional random fields.

Journal: BMC medical informatics and decision making
Published Date: Dec 7, 2018

Abstract

BACKGROUND: Disease named entity recognition (NER) is a fundamental step in information processing of medical texts. However, disease NER involves complex issues such as descriptive modifiers in actual practice. The accurate identification of disease NER is a still an open and essential research problem in medical information extraction and text mining tasks.

Authors

  • Kai Xu
    Department of Anesthesiology, Huai'an Hospital Affiliated to Yangzhou University (The Fifth People's Hospital of Huai'an), Huaian, China.
  • Zhanfan Zhou
    School of Information Science and Technology, Guangdong Universities of Foreign Studies, Guangzhou, China.
  • Tao Gong
    Educational Testing Service, Princeton, NJ, USA.
  • Tianyong Hao
    School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China. haoty@gdufs.edu.cn.
  • Wenyin Liu
    School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China. liuwy@gdut.edu.cn.

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