An imConvNet-based deep learning model for Chinese medical named entity recognition.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: With the development of current medical technology, information management becomes perfect in the medical field. Medical big data analysis is based on a large amount of medical and health data stored in the electronic medical system, such as electronic medical records and medical reports. How to fully exploit the resources of information included in these medical data has always been the subject of research by many scholars. The basis for text mining is named entity recognition (NER), which has its particularities in the medical field, where issues such as inadequate text resources and a large number of professional domain terms continue to face significant challenges in medical NER.

Authors

  • Yuchen Zheng
    Medical College, Guizhou University, Guiyang, 550025, Guizhou, China.
  • Zhenggong Han
    Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China.
  • Yimin Cai
    Medical College, Guizhou University, Guiyang, 550025, Guizhou, China.
  • Xubo Duan
    Medical College, Guizhou University, Guiyang, 550025, Guizhou, China.
  • Jiangling Sun
    Guiyang Hospital of Stomatology, Guiyang, 550002, Guizhou, China.
  • Wei Yang
    Key Laboratory of Structure-Based Drug Design and Discovery (Shenyang Pharmaceutical University), Ministry of Education, School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Wenhua Road 103, Shenyang 110016, PR China. Electronic address: 421063202@qq.com.
  • Haisong Huang
    Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China. hshuang@gzu.edu.cn.