Representation learning for clinical time series prediction tasks in electronic health records.

Journal: BMC medical informatics and decision making
PMID: 31842854

Abstract

BACKGROUND: Electronic health records (EHRs) provide possibilities to improve patient care and facilitate clinical research. However, there are many challenges faced by the applications of EHRs, such as temporality, high dimensionality, sparseness, noise, random error and systematic bias. In particular, temporal information is difficult to effectively use by traditional machine learning methods while the sequential information of EHRs is very useful.

Authors

  • Tong Ruan
    East China University of Science and Technology, Shanghai, China. ruantong@ecust.edu.cn.
  • Liqi Lei
    School of Information Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China.
  • Yangming Zhou
    School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China. Electronic address: ymzhou@ecust.edu.cn.
  • Jie Zhai
    School of Information Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China.
  • Le Zhang
    State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China; College of Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Science and Technology on Particle Materials, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 361021, China.
  • Ping He
    Shanghai Hospital Development Center, Shanghai 200040, China. Electronic address: heping@shdc.org.cn.
  • Ju Gao
    Department of Anesthesiology, Institute of Anesthesia, Emergency and Critical Care, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, 225002 Yangzhou, Jiangsu, China.

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