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.
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This work was supported by US National Science Foundation (Nos. DBI-1356669 and III-1526012).
Recommended by Associate Editor Chandrasekhar Kambhampati
Zhen-Jie Yao received the B. Sc. degree in instrument science from the Zhejiang University, China in 2007, and the Ph. D. degree in communication engineering from University of Chinese Academy of Sciences, China in 2012. In 2012, he was a researcher at China Mobile Research Institute, China. Currently, he is a researcher in Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, China. He has published about 15 refereed journal and conference papers, and applied about 15 patents.
His research interests include machine learning, signal processing and their application in healthcare.
Jie Bi received the B. Sc. degree in biotechnology from Shanghai Jiao Tong University, China in 2013, and the M. Sc. degree in bioinformatics from the University of Chinese Academy of Sciences, China in 2016. Currently, he is a front-end engineer in Rhinotech Limited Liability Company, China. He won first prize of National Undergraduate Innovation Program (2012, China).
Yi-Xin Chen received the Ph. D. degree in computer science from the University of Illinois at Urbana-Champaign (UIUC), USA in 2005. He is a professor of computer science and engineering at the Washington University in St. Louis, USA, which he joined in 2005. He received the Best Paper Award at the Idea, Development, Exploration, Assessment, Long-term Follow-up, Improving the Quality of Research in Surgery Conference (2016), Distinguished Paper Award at the American Medical Informatics Association Conference (2015), Best Student Paper Runner-up Award at the Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) Conference (2014), Best Paper Award at the American Association for Artificial Intelligence Conference on Artificial Intelligence (2010), and the IEEE International Conference on Tools for AI (2005). He also received Best Paper Award nominations at IEEE International Conference on Data Mining (2013), IEEE Real-time and Embedded Technology and Applications Symposium (2012), and ACM SIGKDD Conference (2009). His work on planning has won First Prizes in the International Planning Competitions (2004 and 2006). He received an Early Career Principal Investigator Award from the Department of Energy (2006) and a Microsoft Research New Faculty Fellowship (2007). His research has been funded by National Science Foundation, National Institutes of Health, Department of Energy, Microsoft, Fujitsu, and Memorial Sloan-Kettering Cancer Center. He is an associate editor for ACM Transactions of Intelligent Systems and Technology, Annals of Mathematics and Artificial Intelligence, and Journal of Artificial Intelligence Research. He was an associate editor for IEEE Transactions on Knowledge and Data Engineering from 2008 to 2012.
His research interests include data mining, machine learning, artificial intelligence, and optimization.
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Yao, Z., Bi, J. & Chen, Y. Applying Deep Learning to Individual and Community Health Monitoring Data: A Survey. Int. J. Autom. Comput. 15, 643–655 (2018). https://doi.org/10.1007/s11633-018-1136-9
- Deep learning
- electronic health records (EHR)
- neural networks