MR-KPA: medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Medication recommendation based on electronic medical record (EMR) is a research hot spot in smart healthcare. For developing computational medication recommendation methods based on EMR, an important challenge is the lack of a large number of longitudinal EMR data with time correlation. Faced with this challenge, this paper proposes a new EMR-based medication recommendation model called MR-KPA, which combines knowledge-enhanced pre-training with the deep adversarial network to improve medication recommendation from both feature representation and the fine-tuning process. Firstly, a knowledge-enhanced pre-training visit model is proposed to realize domain knowledge-based external feature fusion and pre-training-based internal feature mining for improving the feature representation. Secondly, a medication recommendation model based on the deep adversarial network is developed to optimize the fine-tuning process of pre-training visit model and alleviate over-fitting of model caused by the task gap between pre-training and recommendation.

Authors

  • Shaofu Lin
    Faculty of Information Technology, Beijing University of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing, 100124, China.
  • Mengzhen Wang
    Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China.
  • Chengyu Shi
  • Zhe Xu
    Thayer School of Engineering at Dartmouth College Hanover NH USA john.zhang@dartmouth.edu.
  • Lihong Chen
    NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100176, China.
  • Qingcai Gao
    Faculty of Information Technology, Beijing University of Technology, Beijing, China.
  • Jianhui Chen
    Faculty of Information Technology, Beijing University of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing, 100124, China. chenjianhui@bjut.edu.cn.