A Drug Recommendation Model Based on Message Propagation and DDI Gating Mechanism.

Journal: IEEE journal of biomedical and health informatics
Published Date:

Abstract

Drug recommendation task based on the deep learning model has been widely studied and applied in the health care field in recent years. However, the accuracy of drug recommendation models still needs to be improved. In addition, the existing recommendation models either give only one recommendation (however, there may be a variety of drug combination options in practice) or can not provide the confidence level of the recommended result. To fill these gaps, a Drug Recommendation model based on Message Propagation neural network (denoted as DRMP) is proposed in this paper. Then, the Drug-Drug Interaction (DDI) knowledge is introduced into the proposed model to reduce the DDI rate in recommended drugs. Finally, the proposed model is extended to Bayesian Neural Network (BNN) to realize multiple recommendations and give the confidence of each recommendation result, so as to provide richer information to help doctors make decisions. Experimental results on public data sets show that the proposed model is superior to the best existing models.

Authors

  • Yongjian Ren
  • Yuliang Shi
    School of Software, Shandong University, China; Dareway Software Co., Ltd, China. Electronic address: shiyuliang@sdu.edu.cn.
  • Kun Zhang
    Philosophy Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
  • Xinjun Wang
    Department of Environmental Design, School of Art and Design, Changzhou Institute of Technology, Changzhou, China.
  • Zhiyong Chen
  • Hui Li
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.