Assistant diagnosis with Chinese electronic medical records based on CNN and BiLSTM with phrase-level and word-level attentions.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Inferring diseases related to the patient's electronic medical records (EMRs) is of great significance for assisting doctor diagnosis. Several recent prediction methods have shown that deep learning-based methods can learn the deep and complex information contained in EMRs. However, they do not consider the discriminative contributions of different phrases and words. Moreover, local information and context information of EMRs should be deeply integrated.

Authors

  • Tong Wang
    School of Public Health, Shanxi Medical University, Taiyuan 030000, China; Key Laboratory of Coal Environmental Pathogenicity and Prevention (Shanxi Medical University), Ministry of Education, Taiyuan 030000, China.
  • Ping Xuan
    School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
  • Zonglin Liu
    School of Computer Science and Technology, Heilongjiang University, Harbin, 150080, China.
  • Tiangang Zhang
    School of Mathematical Science, Heilongjiang University, Harbin 150080, China. zhang@hlju.edu.cn.