Advertisement

Deep Learning in Smart Health: Methodologies, Applications, Challenges

  • Murat Simsek
  • Alex Adim Obinikpo
  • Burak KantarciEmail author
Chapter

Abstract

The advent of artificial intelligence methodologies pave the way towards smarter healthcare by exploiting new concepts such as deep learning. This chapter presents an overview of deep learning techniques that are applied to smart healthcare. Deep learning techniques are frequently applied to smart health to enable AI-based recent technological development to healthcare. Furthermore, the chapter also introduces challenges and opportunities in deep learning particularly in the healthcare domain.

Keywords

Predictive analytics Deep learning Smart health Medical imaging 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    A.H. Abdulnabi, G. Wang, J. Lu, K. Jia, Multi-task CNN model for attribute prediction. IEEE Trans. Multimedia 17(11), 1949–1959 (2015)CrossRefGoogle Scholar
  2. 2.
    D.H. Ackley, G.E. Hinton, T.J. Sejnowski, A learning algorithm for Boltzmann machines. Cogn. Sci. 9(1), 147–169 (1985)CrossRefGoogle Scholar
  3. 3.
    I.N. Aizenberg, N.N. Aizenberg, G.A. Krivosheev, Multi-valued and universal binary neurons: learning algorithms, application to image processing and recognition. Lecture Notes Comput. Sci. 1715(4), 306–316 (1999)CrossRefGoogle Scholar
  4. 4.
    G. Alain, Y. Bengio, S. Rifai, Regularized auto-encoders estimate local statistics, in Proc. CoRR (2012), pp. 1–17Google Scholar
  5. 5.
    B. Alipanahi, A. Delong, M.T. Weirauch, B.J. Frey, Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33(8), 831 (2015)CrossRefGoogle Scholar
  6. 6.
    E. Alpaydin, Introduction to Machine Learning (MIT Press, Cambridge, 2014)zbMATHGoogle Scholar
  7. 7.
    M.M. Baig, H. Gholamhosseini, Smart health monitoring systems: an overview of design and modeling. J. Med. Syst. 37(2), 9898 (2013)Google Scholar
  8. 8.
    Y. Bar, I. Diamant, L. Wolf, H. Greenspan, Deep learning with non-medical training used for chest pathology identification, in SPIE Medical Imaging, vol. 9414 (2015), pp. 94140V-1–94140V-7Google Scholar
  9. 9.
    Y. Chen, Y. Li, R. Narayan, A. Subramanian, X. Xie, Gene expression inference with deep learning. Bioinformatics 32(12), 1832–1839 (2016)CrossRefGoogle Scholar
  10. 10.
    P. Danaee, R. Ghaeini, D.A. Hendrix, A deep learning approach for cancer detection and relevant gene identification, in Pacific Symposium on Biocomputing (World Scientific, Singapore, 2017), pp. 219–229Google Scholar
  11. 11.
    A. De Carvalho, M.C. Fairhurst, D.L. Bisset, An integrated Boolean neural network for pattern classification. Pattern Recogn. Lett. 15(8), 807–813 (1994)CrossRefGoogle Scholar
  12. 12.
    L. Deng, O. Abdelhamid, D. Yu, A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2013), pp. 6669–6673Google Scholar
  13. 13.
    R. Fakoor, F. Ladhak, A. Nazi, M. Huber, Using deep learning to enhance cancer diagnosis and classification, in Proceedings of the International Conference on Machine Learning, vol. 28 (2013)Google Scholar
  14. 14.
    R. Fan, F.-L. Zhang, M. Zhang, R.R. Martin, Robust tracking-by-detection using a selection and completion mechanism. Comput. Visual Media 3(3), 285–294 (2017)CrossRefGoogle Scholar
  15. 15.
    M. Finger, M. Razaghi, Conceptualizing “smart cities”. Informatik-Spektrum 40(1), 6–13 (2017)CrossRefGoogle Scholar
  16. 16.
    M. Frandes, B. Timar, D. Lungeanu, A risk based neural network approach for predictive modeling of blood glucose dynamics. Stud. Health Technol. Inform. 228, 577–581 (2016)Google Scholar
  17. 17.
    K. Fukushima, Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)zbMATHCrossRefGoogle Scholar
  18. 18.
    V. Gulshan, L. Peng, M. Coram, M.C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, et al., Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)CrossRefGoogle Scholar
  19. 19.
    G.E. Hinton, Learning multiple layers of representation. Trends Cogn. Sci. 11(10), 428 (2007)CrossRefGoogle Scholar
  20. 20.
    G.E. Hinton, P. Dayan, B.J. Frey, R.M. Neal, The “wake-sleep” algorithm for unsupervised neural networks. Science 268(5214), 1158 (1995)CrossRefGoogle Scholar
  21. 21.
    G.E. Hinton, S. Osindero, Y.-W. Teh, A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    G. Huang, H. Lee, E. Learnedmiller, Learning hierarchical representations for face verification with convolutional deep belief networks, in 2012 IEEE Conference on Computer Vision and Pattern Recognition (2012), pp. 2518–2525Google Scholar
  24. 24.
    A.G. Ivakhnenko, V.G. Lapa, Cybernetic predicting devices. Transdex (1966)Google Scholar
  25. 25.
    M.I. Jordan, T.M. Mitchell, Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    K. Kamnitsas, C. Ledig, V.F.J. Newcombe, J.P. Simpson, A.D. Kane, D.K. Menon, D. Rueckert, B. Glocker, Efficient Multi-Scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)CrossRefGoogle Scholar
  27. 27.
    Y. Kim, J.W. Chong, K.H. Chon, J. Kim, Wavelet-based AR–SVM for health monitoring of smart structures. Smart Mater. Struct. 22(1), 015003 (2012)CrossRefGoogle Scholar
  28. 28.
    H. Larochelle, Y. Bengio, Classification using discriminative restricted Boltzmann machines, in International Conference (2008), pp. 536–543Google Scholar
  29. 29.
    Y. Lecun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (2014)CrossRefGoogle Scholar
  30. 30.
    T. Lee, S. Yoon, Boosted categorical restricted Boltzmann machine for computational prediction of splice junctions, in International Conference on Machine Learning (2015), pp. 2483–2492Google Scholar
  31. 31.
    M.K.K. Leung, H.Y. Xiong, L.J. Lee, B.J. Frey, Deep learning of the tissue-regulated splicing code. Bioinformatics 30(12), i121–i129 (2014)CrossRefGoogle Scholar
  32. 32.
    J.M. Levy, Contemporary Urban Planning (Taylor & Francis, London, 2016)CrossRefGoogle Scholar
  33. 33.
    C. Li, X. Wang, W. Liu, L.J. Latecki, DeepMitosis: mitosis detection via deep detection, verification and segmentation networks. Med. Image Anal. (2018)Google Scholar
  34. 34.
    S. Liu, H. Zheng, Y. Feng, W. Li, Prostate cancer diagnosis using deep learning with 3D multiparametric MRI, in Medical Imaging 2017: Computer-Aided Diagnosis, vol. 10134 (International Society for Optics and Photonics, Bellingham, 2017), page 1013428Google Scholar
  35. 35.
    F.S. Lu, S. Hou, K. Baltrusaitis, M. Shah, J. Leskovec, R. Sosic, J. Hawkins, J. Brownstein, G. Conidi, J. Gunn, J. Gray, A. Zink, M. Santillana, Accurate influenza monitoring and forecasting using novel internet data streams: a case study in the Boston Metropolis. JMIR Public Health Surveill. 4(1), e4 (2018)CrossRefGoogle Scholar
  36. 36.
    J. Lyons, A. Dehzangi, R. Heffernan, A. Sharma, K. Paliwal, A. Sattar, Y. Zhou, Y. Yang, Predicting backbone cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network. J. Comput. Chem. 35(28), 2040–2046 (2014)CrossRefGoogle Scholar
  37. 37.
    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. Sci. Rep. 6(1), 26094 (2016)Google Scholar
  38. 38.
    M. Mohri, A. Rostamizadeh, A. Talwalkar, Foundations of Machine Learning (MIT Press, Cambridge, 2012)zbMATHGoogle Scholar
  39. 39.
    A. Obinikpo, B. Kantarci, Big sensed data meets deep learning for smarter health care in smart cities. J. Sens. Actuator Netw. 6(4), 22 (2017)CrossRefGoogle Scholar
  40. 40.
    T. Pham, T. Tran, D. Phung, S. Venkatesh, DeepCare: a deep dynamic memory model for predictive medicine, in Advances in Knowledge Discovery and Data Mining. PAKDD 2016, ed. by J. Bailey, L. Khan, T. Washio, G. Dobbie, J. Huang, R. Wang. Lecture Notes in Computer Science, vol. 9652 (Springer, Cham, 2016)CrossRefGoogle Scholar
  41. 41.
    S. Poslad, Ubiquitous Computing: Smart Devices, Environments and Interactions (Wiley, New York, 2011)Google Scholar
  42. 42.
    S. Rifai, Y. Bengio, Y. Dauphin, P. Vincent, A generative process for sampling contractive auto-encoders (2012). Preprint arXiv:1206.6434Google Scholar
  43. 43.
    F. Rosenblatt, The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386 (1958)CrossRefGoogle Scholar
  44. 44.
    R. Salakhutdinov, A. Mnih, G. Hinton, Restricted Boltzmann machines for collaborative filtering, in International Conference on Machine Learning (2007), pp. 791–798Google Scholar
  45. 45.
    J. Schmidhuber, Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2014)CrossRefGoogle Scholar
  46. 46.
    J. Schmidhuber, Learning complex, extended sequences using the principle of history compression. Neural Comput. 4(2), 234–242 (2014)CrossRefGoogle Scholar
  47. 47.
    M. Shah, C. Rubadue, D. Suster, D. Wang, Deep learning assessment of tumor proliferation in breast cancer histological images (2016). Preprint arXiv:1610.03467Google Scholar
  48. 48.
    H.-C.C. Shin, M.R. Orton, D.J. Collins, S.J. Doran, M.O. Leach, Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1930–1943 (2013)CrossRefGoogle Scholar
  49. 49.
    P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(Dec), 3371–3408 (2010)MathSciNetzbMATHGoogle Scholar
  50. 50.
    B. Wei, Z. Han, X. He, Y. Yin, Deep learning model based breast cancer histopathological image classification, in 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (IEEE, Piscataway, 2017), pp. 348–353Google Scholar
  51. 51.
    M.N. Wernick, Y. Yang, J.G. Brankov, G. Yourganov, S.C. Strother, Machine learning in medical imaging. IEEE Signal Process. Mag. 27(4), 25–38 (2010)CrossRefGoogle Scholar
  52. 52.
    S. Zhang, J. Zhou, H. Hu, H. Gong, L. Chen, C. Cheng, J. Zeng, A deep learning framework for modeling structural features of RNA-binding protein targets. Nucleic Acids Res. 44(4), e32 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Murat Simsek
    • 1
    • 2
  • Alex Adim Obinikpo
    • 3
  • Burak Kantarci
    • 3
    Email author
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  2. 2.Department of Astronautical EngineeringIstanbul Technical UniversityIstanbulTurkey
  3. 3.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

Personalised recommendations