Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies.

Journal: Journal of biomedical informatics
Published Date: Dec 30, 2021

Abstract

OBJECTIVE: Temporal electronic health records (EHRs) contain a wealth of information for secondary uses, such as clinical events prediction and chronic disease management. However, challenges exist for temporal data representation. We therefore sought to identify these challenges and evaluate novel methodologies for addressing them through a systematic examination of deep learning solutions.

Authors

  • Feng Xie
    School of Computer Science, Guangdong University of Technology, Guangzhou, China. Electronic address: 1111705006@mail2.gdut.edu.cn.
  • Han Yuan
    Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Yilin Ning
    Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
  • Marcus Eng Hock Ong
    Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore. marcus.ong.e.h@sgh.com.sg.
  • Mengling Feng
    Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore.
  • Wynne Hsu
    School of Computing, National University of Singapore.
  • Bibhas Chakraborty
    Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
  • Nan Liu
    Duke-NUS Medical School Centre for Quantitative Medicine Singapore Singapore.

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