  Objective: Temporal electronic health records (EHRs) can be a wealth of
information for secondary uses, such as clinical events prediction or 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.
  Methods: We searched five databases (PubMed, EMBASE, the Institute of
Electrical and Electronics Engineers [IEEE] Xplore Digital Library, the
Association for Computing Machinery [ACM] digital library, and Web of Science)
complemented with hand-searching in several prestigious computer science
conference proceedings. We sought articles that reported deep learning
methodologies on temporal data representation in structured EHR data from
January 1, 2010, to August 30, 2020. We summarized and analyzed the selected
articles from three perspectives: nature of time series, methodology, and model
implementation.
  Results: We included 98 articles related to temporal data representation
using deep learning. Four major challenges were identified, including data
irregularity, data heterogeneity, data sparsity, and model opacity. We then
studied how deep learning techniques were applied to address these challenges.
Finally, we discuss some open challenges arising from deep learning.
  Conclusion: Temporal EHR data present several major challenges for clinical
prediction modeling and data utilization. To some extent, current deep learning
solutions can address these challenges. Future studies can consider designing
comprehensive and integrated solutions. Moreover, researchers should
incorporate additional clinical domain knowledge into study designs and enhance
the interpretability of the model to facilitate its implementation in clinical
practice.
