A Survey of Deep Learning for Electronic Health Records

Jiabao Xu, Xuefeng Xi*, Jie Chen, Victor S. Sheng*, Jieming Ma, Zhiming Cui

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

12 Citations (Scopus)

Abstract

Medical data is an important part of modern medicine. However, with the rapid increase in the amount of data, it has become hard to use this data effectively. The development of machine learning, such as feature engineering, enables researchers to capture and extract valuable information from medical data. Many deep learning methods are conducted to handle various subtasks of EHR from the view of information extraction and representation learning. This survey designs a taxonomy to summarize and introduce the existing deep learning-based methods on EHR, which could be divided into four types (Information Extraction, Representation Learning, Medical Prediction and Privacy Protection). Furthermore, we summarize the most recognized EHR datasets, MIMIC, eICU, PCORnet, Open NHS, NCBI-disease and i2b2/n2c2 NLP Research Data Sets, and introduce the labeling scheme of these datasets. Furthermore, we provide an overview of deep learning models in various EHR applications. Finally, we conclude the challenges that EHR tasks face and identify avenues of future deep EHR research.

Original languageEnglish
Article number11709
JournalApplied Sciences (Switzerland)
Volume12
Issue number22
DOIs
Publication statusPublished - Nov 2022

Keywords

  • de-identification
  • deep EHR
  • deep learning
  • electronic health records (EHR)
  • machine learning (ML)
  • natural language processing (NLP)
  • privacy preservation

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