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International Journal of Automation and Computing

, Volume 15, Issue 6, pp 643–655 | Cite as

Applying Deep Learning to Individual and Community Health Monitoring Data: A Survey

  • Zhen-Jie YaoEmail author
  • Jie Bi
  • Yi-Xin Chen
Review

Abstract

In the recent years, deep learning models have addressed many problems in various fields. Meanwhile, technology development has spawned the big data in healthcare rapidly. Nowadays, application of deep learning to solve the problems in healthcare is a hot research direction. This paper introduces the application of deep learning in healthcare extensively. We focus on 7 application areas of deep learning, which are electronic health records (EHR), electrocardiography (ECG), electroencephalogram (EEG), community healthcare, data from wearable devices, drug analysis and genomics analysis. The scope of this paper does not cover medical image processing since other researchers have already substantially reviewed it. In addition, we analyze the merits and drawbacks of the existing works, analyze the existing challenges, and discuss future trends.

Keywords

Deep learning healthcare electronic health records (EHR) neural networks survey 

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Notes

Acknowledgements

This work was supported by US National Science Foundation (Nos. DBI-1356669 and III-1526012).

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Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Beijing Advanced Innovation Center for Future Internet TechnologyBeijing University of TechnologyBeijingChina
  2. 2.Department of Computer Science and EngineeringWashington University in St. LouisSt. LouisUSA

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