Study on the semi-supervised learning-based patient similarity from heterogeneous electronic medical records.

Journal: BMC medical informatics and decision making
Published Date: Jul 30, 2021

Abstract

BACKGROUND: A new learning-based patient similarity measurement was proposed to measure patients' similarity for heterogeneous electronic medical records (EMRs) data.

Authors

  • Ni Wang
    School of Biomedical Engineering, Capital Medical University, Beijing, China.
  • Yanqun Huang
    School of Biomedical Engineering, Capital Medical University, Beijing, China.
  • Honglei Liu
    School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China. liuhonglei@ccmu.edu.cn.
  • Zhiqiang Zhang
    Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
  • Lan Wei
    Information Center, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, People's Republic of China.
  • Xiaolu Fei
    Information Center, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, People's Republic of China. feixiaolu@xwh.ccmu.edu.cn.
  • Hui Chen
    Xiangyang Central HospitalAffiliated Hospital of Hubei University of Arts and Science Xiangyang 441000 China.

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