CNN-based ranking for biomedical entity normalization.

Journal: BMC bioinformatics
Published Date: Oct 3, 2017

Abstract

BACKGROUND: Most state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel convolutional neural network (CNN) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities.

Authors

  • Haodi Li
    Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China. Electronic address: haodili.hit@gmail.com.
  • Qingcai Chen
    Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.
  • Buzhou Tang
  • Xiaolong Wang
    Cardiovascular Department, Shuguang Hospital Affiliated to Shanghai University of TCM Shanghai, China.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Baohua Wang
    College of Mathematics and statistics, Shenzhen University, Shenzhen, GuangDong, China.
  • Dong Huang
    Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology, Shenzhen, GuangDong, China.

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