Max-margin weight learning for medical knowledge network.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 11, 2018

Abstract

BACKGROUND AND OBJECTIVE: The application of medical knowledge strongly affects the performance of intelligent diagnosis, and method of learning the weights of medical knowledge plays a substantial role in probabilistic graphical models (PGMs). The purpose of this study is to investigate a discriminative weight-learning method based on a medical knowledge network (MKN).

Authors

  • Jingchi Jiang
    School of Computer Science and Technology, Harbin Institute of Technology, Integrated Laboratory Building 803, Harbin 150001, China. Electronic address: jiangjingchi0118@163.com.
  • Jing Xie
    Department of Critical Care Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Chao Zhao
    Department of Hygienic Inspection, School of Public Health, Jilin University 1163 Xinmin Street Changchun 130021 Jilin China songxiuling@jlu.edu.cn li_juan@jlu.edu.cn jinmh@jlu.edu.cn +86 43185619441.
  • Jia Su
    Language Technology Research Center, Harbin Institute of Technology, School of Computer Science and Technology, No. 92 West Dazhi Street, Harbin, Heilongjiang, China.
  • Yi Guan
    School of Computer Science and Technology, Harbin Institute of Technology, Integrated Laboratory Building 803, Harbin 150001, China. Electronic address: guanyi@hit.edu.cn.
  • Qiubin Yu
    Medical Record Room, Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China. Electronic address: yuqiubin6695@163.com.

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