A hybrid method based on semi-supervised learning for relation extraction in Chinese EMRs.

Journal: BMC medical informatics and decision making
Published Date: Jun 27, 2022

Abstract

BACKGROUND: Building a large-scale medical knowledge graphs needs to automatically extract the relations between entities from electronic medical records (EMRs) . The main challenges are the scarcity of available labeled corpus and the identification of complexity semantic relations in text of Chinese EMRs. A hybrid method based on semi-supervised learning is proposed to extract the medical entity relations from small-scale complex Chinese EMRs.

Authors

  • Chunming Yang
    School of Computer Science and Technology, Southwest University of Science & Technology, Mianyang 621010, Sichuan, China.
  • Dan Xiao
    School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, 621010, Sichuan, China.
  • YuanYuan Luo
    Department of Ophthalmology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai 200127, China.
  • Bo Li
    Electric Power Research Institute, Yunnan Power Grid Co., Ltd., Kunming, Yunnan, China.
  • Xujian Zhao
    School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, 621010, Sichuan, China.
  • Hui Zhang
    Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China.

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