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.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Y. Le Cun, Y. Bengio, G. Hinton. Deep learning. Nature, vol. 521, no. 7553, pp. 436–444, 2015. DOI: https://doi.org/10.1038/nature14539.
G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, C. I. Sánchez. A survey on deep learning in medical image analysis. Medical Image Analysis, vol. 42, pp. 60–88, 2017.
H. Greenspan, B. van Ginneken, R. M. Summers. Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1153–1159, 2016. DOI: https://doi.org/10.1109/TMI.2016.2553401.
D. Ravì, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo, G. Z. Yang. Deep learning for health informatics. IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 4–21, 2017. DOI: https://doi.org/10.1109/JBHI.2016.2636665.
F. Rosenblatt. The Perceptron: A Perceiving and Recognizing Automaton, Report 85-60-1. Cornell Aeronautical Laboratory, Buffalo, New York, USA, 1957.
P. J. Werbos. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, Ph. D. dissertation, Harvard University, Harvard, USA, 1974.
D. E. Rumelhart, G. E. Hinton, R. J. Williams. Learning representations by back-propagating errors. Cognitive Modeling, vol. 5, no. 3, pp. 533–536, 1988.
Y. Lecun, L. Bottou, Y. Bengio, P. Haffner. Gradientbased learning applied to document recognition. Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998. DOI: https://doi.org/10.1109/5.726791.
G. E. Hinton, S. Osindero, Y. W. Teh. A fast learning algorithm for deep belief nets. Neural Computation, vol. 18, no. 7, pp. 1527–1554, 2006. DOI: https://doi.org/10.1162/neco.2006.18.7.1527.
A. Krizhevsky, I. Sutskever, G. Hinton. Imagenet classification with deep convolutional neural networks. In Proceedings of Advances in Neural Information Processing Systems, Lake Tahoe, USA, pp. 1097–1105, 2012.
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. R. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, B. Kingsbury. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82–97, 2012. DOI: https://doi.org/10.1109/MSP.2012.2205597.
R. Miotto, L. Li, B. A. Kidd, J. T. Dudley. Deep patient: An unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports, vol. 6, Article number 26094, 2016. DOI: https://doi.org/10.1038/srep26094.
P. Danaee, R. Ghaeini, D. A. Hendrix. A deep learning approach for cancer detection and relevant gene identification. In Proceedings of Pacific Symposium on Biocomputing, World Scientific, Kohala Coast, USA, 2017.
M. M. Al Rahhal, Y. Bazi, H. AlHichri, N. Alajlan, F. Melgani, R. R. Yager. Deep learning approach for active classification of electrocardiogram signals. Information Sciences, vol. 345, pp. 340–354, 2016. DOI: https://doi.org/10.1016/j.ins.2016.01.082.
Z. Xu, S. Wang, F. Y. Zhu, J. Z. Huang. Seq2seq fingerprint: An unsupervised deep molecular embedding for drug discovery. In Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Boston, USA, pp. 285–294, 2017. DOI: https://doi.org/10.1145/3107411.3107424.
G. E. Hinton, T. J. Sejnowski. Learning and relearning in Boltzmann machines. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. E. Rumelhart, J. L. McClelland, Eds., Cambridge, USA: MIT Press, pp. 1, 1986.
R. Salakhutdinov, G. E. Hinton. Deep Boltzmann machines. In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, Clearwater Beach, USA, pp. 448–455, 2009.
R. J. Williams, D. Zipser. A learning algorithm for continually running fully recurrent neural networks. Neural Computation, vol. 1, no. 2, pp. 270–280, 1989. DOI: https://doi.org/10.1162/neco.1989.1.2.270.
S. Hochreiter, J. Schmidhuber. Long short-term memory. Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. DOI: https://doi.org/10.1162/neco.1997.9.8.1735.
J. Chung, C. Gulcehre, K. Cho, Y. Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. https://doi.org/arxiv.org/abs/1412.3555, 2014. (ar**v: 1412.3555)
Z. Liang, G. Zhang, J. X. Huang, Q. V. Hu. Deep learning for healthcare decision making with EMRs. In Proceedings of IEEE International Conference on Bioinformatics and Biomedicine, Belfast, UK, pp. 556–559, 2014. DOI: https://doi.org/10.1109/BIBM.2014.6999219.
Z. P. Che, S. Purushotham, R. Khemani, Y. Liu. Distilling knowledge from deep networks with applications to healthcare domain. [Online], Available: https://doi.org/arxiv.org/abs/1512.03542, 2015.
A. N. Jagannatha, H. Yu. Bidirectional RNN for medical event detection in electronic health records. In Proceedings of Conference Association for Computational Linguistics, North American Chapter, Berlin, Germany, pp. 473–482, 2016.
Z. C. Lipton, D. C. Kale, C. Elkan, R. Wetzel. Learning to diagnose with LSTM recurrent neural networks. https://doi.org/arxiv.org/abs/1511.03677, 2015.
C. Esteban, O. Staeck, S. Baier, Y. C. Yang, V. Tresp. Predicting clinical events by combining static and dynamic information using recurrent neural networks. In Proceedings of IEEE International Conference on Healthcare Informatics, Chicago, USA, pp. 93–101, 2016. DOI: https://doi.org/10.1109/ICHI.2016.16.
Z. P. Che, S. Purushotham, K. Cho, D. Sontag, Y. Liu. Recurrent neural networks for multivariate time series with missing values. [Online], Available: https://doi.org/arxiv.org/abs/1606.01865, 2016.
S. Mehrabi, S. Sohn, D. H. Li, J. J. Pankratz, T. Therneau, J. L. S. Sauver, H. F. Liu, M. Palakal. Temporal pattern and association discovery of diagnosis codes using deep learning. In Proceedings of International Conference on Healthcare Informatics, Dallas, USA, pp. 408–416, 2015. DOI: https://doi.org/10.1109/ICHI.2015.58.
J. Futoma, J. Morris, J. Lucas. A comparison of models for predicting early hospital readmissions. Journal of Biomedical Informatics, vol. 56, pp. 229–238, 2015. DOI: https://doi.org/10.1016/j.jbi.2015.05.016.
E. Putin, P. Mamoshina, A. Aliper, M. Korzinkin, A. Moskalev, A. Kolosov, A. Ostrovskiy, C. Cantor, J. Vijg, A. Zhavoronkov. Deep biomarkers of human aging: Application of deep neural networks to biomarker development. AGING, vol. 8, no. 5, pp. 1021–1033, 2016. DOI: https://doi.org/10.18632/aging.100968.
Y. Cheng, F. Wang, P. Zhang, J. Y. Hu. Risk prediction with electronic health records: A deep learning approach. In Proceedings of SIAM International Conference on Data Mining, Miami, USA, pp. 432–440, 2016.
E. Choi, A. Schuetz, W. F. Stewart, J. M. Sun. Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, vol. 24, no. 2, pp. 361–370, 2017. DOI: https://doi.org/10.1093/jamia/ocw112.
T. Pham, T. Tran, D. Phung, S. Venkatesh. DeepCare: A deep dynamic memory model for predictive medicine. In Proceedings of the 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Auckland, New Zealand, pp. 30–41, 2016. DOI: https://doi.org/10.1007/978-3-319-31750-2_3.
A. Avati, K. Jung, S. Harman, L. Downing, A. Ng, N. H. Shah. Improving palliative care with deep learning. [Online], Available: https://doi.org/arxiv.org/abs/1711.06402, 2017.
A. Rajkomar, E. Oren, K. Chen, A. M. Dai, N. Hajaj, M. Hardt, P. J. Liu, X. B. Liu, J. Marcus, M. M. Sun, P. Sundberg, H. Yee, K. Zhang, Y. Zhang, G. Flores, G. E. Duggan, J. Irvine, Q. Le, K. Litsch, A. Mossin, J. Tansuwan, D. Wang, J. Wexler, J. Wilson, D. Ludwig, S. L. Volchenboum, K. Chou, M. Pearson, S. Madabushi, N. H. Shah, A. J. Butte, M. D. Howell, C. Cui, G. S. Corrado, J. Dean. Scalable and accurate deep learning with electronic health records. Nature Partner Journals Digital Medicine, vol. 1, pp. 1–10, 2018. DOI: https://doi.org/10.1038/s41746-018-0029-1.
F. Dernoncourt, J. Y. Lee, O. Uzuner, P. Szolovits. Deidentification of patient notes with recurrent neural networks. Journal of the American Medical Informatics Association, vol. 24, no. 3, pp. 596–606, 2017. DOI: https://doi.org/10.1093/jamia/ocw156.
S. Chauhan, L. Vig. Anomaly detection in ECG time signals via deep long short-term memory networks. In Proceedings of IEEE International Conference on Data Science and Advanced Analytics, Paris, France, 2015. DOI: https://doi.org/10.1109/DSAA.2015.7344872.
Y. Yan, X. B. Qin, Y. G. Wu, N. N. Zhang, J. P. Fan, L. Wang. A restricted Boltzmann machine based two-lead electrocardiography classification. In Proceedings of the 12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, Cambridge, USA, pp. 1–9, 2015. DOI: https://doi.org/10.1109/BSN.2015.7299399.
U. R. Acharya, H. Fujita, S. L. Oh, Y. Hagiwara, J. H. Tan, M. Adam. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Information Sciences, vol. 415–416, pp. 190–198, 2017. DOI: https://doi.org/10.1016/j.ins.2017.06.027.
Z. J. Yao, Z. Y. Zhu, Y. X. Chen. Atrial fibrillation detection by multi-scale convolutional neural networks. In Proceedings of the 20th International Conference on Information Fusion, **’an, China, pp. 1–6, 2017. DOI: https://doi.org/10.23919/ICIF.2017.8009782.
P. Rajpurkar, A. Y. Hannun, M. Haghpanahi, C. Bourn, A. Y. Ng. Cardiologist-level arrhythmia detection with convolutional neural networks. https://doi.org/arxiv.org/abs/1707.01836, 2017.
D. Wulsin, J. Blanco, R. Mani, B. Litt. Semi-supervised anomaly detection for EEG waveforms using deep belief nets. In Proceedings of the 9th International Conference on Machine Learning and Applications, Washington DC, USA, pp. 436–441, 2010. DOI: https://doi.org/10.1109/ICMLA.2010.71.
A. Page, J. Turner, T. Mohsenin, T. Oates. Comparing raw data and feature extraction for seizure detection with deep learning methods. In Proceedings of the 27th International Flairs Conference, AAAI, Pensacola Beach, USA, pp. 284–287, 2014.
X. W. Jia, K. Li, X. Y. Li, A. D. Zhang. A novel semi-supervised deep learning framework for affective state recognition on EEG signals. In Proceedings of IEEE International Conference on Bioinformatics and Bioengineering, Boca Raton, US, pp. 30–37, 2014. DOI: https://doi.org/10.1109/BIBE.2014.26.
I. Sturm, S. Lapuschkin, W. Samek, K. R. Muller. Interpretable deep neural networks for single-trial EEG classification. Journal of Neuroscience Methods, vol. 274, pp. 141–145, 2016. DOI: https://doi.org/10.1016/j.jneumeth.2016.10.008.
R. T. Schirrmeister, J. T. Springenberg, L. D. J. Fiederer, M. Glasstetter, K. Eggensperger, M. Tangermann, F. Hutter, W. Burgard, T. Ball. Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Map**, vol. 38, no. 11, pp. 5391–5420, 2017. DOI: https://doi.org/10.1002/hbm.23730.
L. Q. Nie, M. Wang, L. M. Zhang, S. C. Yan, B. Zhang, T. S. Chua. Disease inference from health-related questions via sparse deep learning. IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 8, pp. 2107–2119, 2015. DOI: https://doi.org/10.1109/TKDE.2015.2399298.
L. Zhao, J. Z. Chen, F. Chen, W. Wang, C. T. Lu, N. Ramakrishnan. Simnest: Social media nested epidemic simulation via online semi-supervised deep learning. In Proceedings of IEEE International Conference on Data Mining, IEEE, Atlantic City, USA, pp. 639–648, 2015. DOI: https://doi.org/10.1109/ICDM.2015.39.
B. Zou, V. Lampos, R. Gorton, I. J. Cox. On infectious intestinal disease surveillance using social media content. In Proceedings of the 6th International Conference on Digital Health Conference, ACM, Montreal, Canada, pp. 157–161, 2016. DOI: https://doi.org/10.1145/2896338.2896372.
A. Benton, M. Mitchell, D. Hovy. Multi-task learning for mental health using social media text. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain, pp. 1–11 2017.
N. Hammerla, J. Fisher, P. Andras, L. Rochester, R. Walker, T. Ploetz. PD disease state assessment in naturalistic environments using deep learning. In Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, USA, pp. 1742–1748, 2015.
D. Ravi, C. Wong, B. Lo, G. Z. Yang. Deep learning for human activity recognition: A resource efficient implementation on low-power devices. In Proceedings of the 13th IEEE International Conference on Wearable and Implantable Body Sensor Networks, San Francisco, USA, pp. 71–76, 2016. DOI: https://doi.org/10.1109/BSN.2016.7516235.
A. Aliamiri, Y. C. Shen. Deep learning based atrial fibrillation detection using wearable photoplethysmography sensor. In Proceedings of IEEE EMBS International Conference on Biomedical & Health Informatics, Las Vegas, USA, pp. 442–445, 2018. DOI: https://doi.org/10.1109/BHI.2018.8333463.
Q. Zhang, X. X. Chen, Q. Y. Zhan, T. Yang, S. H. **a. Respiration-based emotion recognition with deep learning. Computers in Industry, vol. 92–93, pp. 84–90, 2017. DOI: https://doi.org/10.1016/j.compind.2017.04.005.
Y. J. Chen, T. S. Chen, Z. W. Xu, N. H. Sun, O. Temam. DianNao family: Energy-efficient hardware accelerators for machine learning. Communications of the ACM, vol. 59, no. 11, pp. 105–112, 2016. DOI: https://doi.org/10.1145/2996864.
T. Unterthiner, A. Mayr, G. Klambauer, S. Hochreiter. Toxicity prediction using deep learning. [Online], Available: https://doi.org/arxiv.org/abs/1503.01445, 2015.
J. S. Ma, R. P. Sheridan, A. Liaw, G. E. Dahl, V. Svetnik. Deep neural nets as a method for quantitative structureactivity relationships. Journal of Chemical Information and Modeling, vol. 55, no. 2, pp. 263–274, 2015. DOI: https://doi.org/10.1021/ci500747n.
T. Huynh, Y. L. He, A. Willis, S. Ruger. Adverse drug reaction classification with deep neural networks. In Proceedings of the 26th International Conference on Computational Linguistics, Osaka, Japan, pp. 877–887, 2016.
K. Chaudhary, O. B. Poirion, L. Q. Lu, L. X. Garmire. Deep learning-based multiomics integration robustly predicts survival in liver cancer. Clinical Cancer Research, vol. 24, no. 6, pp. 1248–1259, 2017. DOI: https://doi.org/10.1158/1078-0432.CCR-17-0853..
S. Yousefi, F. Amrollahi, M. Amgad, C. L. Dong, J. E. Lewis, C. Z. Song, D. A. Gutman, S. H. Halani, J. E. V. Vega, D. J. Brat, L. A. D. Cooper. Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models. Scientific Reports, vol. 7, Article number 11707, 2017. DOI: https://doi.org/10.1038/s41598-017-11817-6.
W. L. Chen, J. Wilson, S. Tyree, K. Weinberger, Y. X. Chen. Compressing neural networks with the hashing trick. In Proceedings of the 23nd International Conference on Machine Learning, Lille, France, pp. 2285–2294, 2015.
W. L. Chen, J. Wilson, S. Tyree, K. Q. Weinberger, Y. X. Chen. Compressing convolutional neural networks in the frequency domain. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Francisco, USA, pp. 1475–1484, 2016. DOI: https://doi.org/10.1145/2939672.2939839.
Acknowledgements
This work was supported by US National Science Foundation (Nos. DBI-1356669 and III-1526012).
Author information
Authors and Affiliations
Corresponding author
Additional information
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 Bei**g Advanced Innovation Center for Future Internet Technology, Bei**g 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).
His research interests include machine learning methods for data visualization, Javascript, CSS3, HTML5.
Yi-**n 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.
Rights and permissions
About this article
Cite this article
Yao, ZJ., Bi, J. & Chen, YX. 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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11633-018-1136-9