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Deep Learning in Healthcare: Applications, Challenges, and Opportunities

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Next Generation Healthcare Informatics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1039))

Abstract

Obtaining information and real-time insights from complicated, high-dimensional, and diverse biological data is a major problem in healthcare transformation. In current biomedical research, numerous types of data have emerged, such as imaging, electronic health records, text, and sensor data, all of which are complicated, diverse, inadequately annotated, and usually unstructured. Statistical learning and traditional data mining techniques often need feature engineering to generate useful and resilient features from data, followed by the construction of clustering or prediction models on top of them. In the case of complex data and a lack of subject knowledge, both processes provide numerous obstacles. The most recent breakthroughs in deep learning technology offer an operative paradigms for obtaining learning models from large and complex data. Deep learning algorithms could be used to translate large amounts of biomedical data into enhanced human health. The challenges and opportunities for enhanced technique and applications, particularly in terms of easiness for domain experts and citizen researchers, are highlighted in this chapter. This chapter also discusses how to create comprehensive and relevant easily understandable frameworks to integrate human understandability and deep learning models.

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References

  1. Bhatia, K., & Syal, R. (2017, October). Predictive analysis using hybrid clustering in diabetes diagnosis. In 2017 Recent Developments in Control, Automation & Power Engineering (RDCAPE) (pp. 447–452). IEEE.

    Google Scholar 

  2. Nienhold, D., Dornberger, R., & Korkut, S. (2017, August). Pattern recognition for automated healthcare assessment using non-invasive, ambient sensors. In 2017 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 189–197). IEEE.

    Google Scholar 

  3. Bhardwaj, R., Nambiar, A. R., & Dutta, D. (2017, July). A study of machine learning in healthcare. In 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) (Vol. 2, pp. 236–241). IEEE.

    Google Scholar 

  4. Li, Y., Wu, F. X., & Ngom, A. (2018). A review on machine learning principles for multi-view biological data integration. Briefings in Bioinformatics, 19(2), 325–340.

    Google Scholar 

  5. Bhattacharyya, S., Snasel, V., Hassanian, A. E., Saha, S., & Tripathy, B. K. (2020). Deep learning research with engineering applications. De Gruyter Publications.

    Google Scholar 

  6. Kamath, U., Liu, J., & Whitaker, J. (2019). Deep learning for NLP and speech recognition (Vol. 84). Springer.

    Book  Google Scholar 

  7. Bali, J., Garg, R., & Bali, R. T. (2019). Artificial intelligence (AI) in healthcare and biomedical research: Why a strong computational/AI bioethics framework is required? Indian Journal of Ophthalmology, 67(1), 3.

    Article  Google Scholar 

  8. High, R. (2012). The era of cognitive systems: An inside look at IBM Watson and how it works. IBM Corporation, Redbooks, 1, 16.

    Google Scholar 

  9. Hsu, J. (2016). For sale: Deep learning [News]. IEEE Spectrum, 53(8), 12–13.

    Article  Google Scholar 

  10. Deep learning applications in healthcare (https://www.analyticsinsight.net/these-are-the-top-applications-of-deep-learning-in-healthcare/)

  11. Liang, Z., Zhang, G., Huang, J. X., & Hu, Q. V. (2014, November). Deep learning for healthcare decision making with EMRs. In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 556–559). IEEE.

    Google Scholar 

  12. Alghamdi, A., et al. (2020). Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities. Multimedia Tools and Applications, 1–22.

    Google Scholar 

  13. Wang, X. (2016). Deep learning in object recognition, detection, and segmentation. Foundations and Trends in Signal Processing, 8(4), 217–382.

    Article  Google Scholar 

  14. Purwins, H., Li, B., Virtanen, T., Schlüter, J., Chang, S. Y., & Sainath, T. (2019). Deep learning for audio signal processing. IEEE Journal of Selected Topics in Signal Processing, 13(2), 206–219.

    Article  Google Scholar 

  15. Gardner, M., et al. (2018). Allennlp: A deep semantic natural language processing platform. arXiv preprint arXiv:1803.07640

  16. Wang, Q., Li, B., Xiao, T., Zhu, J., Li, C., Wong, D. F., & Chao, L. S. (2019). Learning deep transformer models for machine translation. arXiv preprint arXiv:1906.01787

  17. Mohsen, H., El-Dahshan, E. S. A., El-Horbaty, E. S. M., & Salem, A. B. M. (2018). Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 3(1), 68–71.

    Article  Google Scholar 

  18. Dorj, U. O., Lee, K. K., Choi, J. Y., & Lee, M. (2018). The skin cancer classification using deep convolutional neural network. Multimedia Tools and Applications, 77(8), 9909–9924.

    Article  Google Scholar 

  19. Zhao, B., Katuwawala, A., Oldfield, C. J., Hu, G., Wu, Z., Uversky, V. N., & Kurgan, L. (2021). Intrinsic disorder in human RNA-binding proteins. Journal of Molecular Biology, 433(21), 167229.

    Article  Google Scholar 

  20. Convolutional neural networks (https://www.analyticsvidhya.com/blog/2020/10/what-is-the-convolutional-neural-network-architecture/)

  21. Recurrent neural networks (https://www.sciencedirect.com/topics/computer-science/recurrent-neural-network)

  22. Auto Encoder (https://machinelearningmastery.com/autoencoder-for-classification/#:~:text=Autoencoder%20is%20a%20type%20of,version%20provided%20by%20the%20encoder.)

  23. Mansourifar, H., & Shi, W. (2020). Deep synthetic minority over-sampling technique. arXiv preprint arXiv:2003.09788

  24. Bai, Y., Bhattacharyya, S. S., Happonen, A. P., & Huttunen, H. (2018, September). Elastic neural networks: A scalable framework for embedded computer vision. In 2018 26th European Signal Processing Conference (EUSIPCO) (pp. 1472–1476). IEEE.

    Google Scholar 

  25. Electronic health record (https://www.healthit.gov/faq/what-electronic-health-record-ehr)

  26. Clinical imaging (https://www.clinicalimaging.org/)

  27. Genomics (https://www.genome.gov/about-genomics/fact-sheets/A-Brief-Guide-to-Genomics)

  28. Lauritsen, S. M., Kalør, M. E., Kongsgaard, E. L., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020). Early detection of sepsis utilizing deep learning on electronic health record event sequences. Artificial Intelligence in Medicine, 104, 101820.

    Article  Google Scholar 

  29. Norgeot, B., et al. (2019). Assessment of a deep learning model based on electronic health record data to forecast clinical outcomes in patients with rheumatoid arthritis. JAMA Network Open, 2(3), e190606–e190606.

    Article  Google Scholar 

  30. Sankaranarayanan, S., et al. (2021). Covid-19 mortality prediction from deep learning in a large multistate electronic health record and laboratory information system data set: Algorithm development and validation. Journal of Medical Internet Research, 23(9), e30157.

    Article  Google Scholar 

  31. Zhao, J., et al. (2019). Learning from longitudinal data in electronic health record and genetic data to improve cardiovascular event prediction. Scientific Reports, 9(1), 1–10.

    Google Scholar 

  32. Ashfaq, A., Sant’Anna, A., Lingman, M., & Nowaczyk, S. (2019). Readmission prediction using deep learning on electronic health records. Journal of Biomedical Informatics, 97, 103256.

    Google Scholar 

  33. Li, R., Hu, B., Liu, F., Liu, W., Cunningham, F., McManus, D. D., & Yu, H. (2019). Detection of bleeding events in electronic health record notes using convolutional neural network models enhanced with recurrent neural network autoencoders: Deep learning approach. JMIR Medical Informatics, 7(1), e10788.

    Article  Google Scholar 

  34. Nguyen, B. P., et al. (2019). Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records. Computer Methods and Programs in Biomedicine, 182, 105055.

    Article  Google Scholar 

  35. Obeid, J. S., et al. (2020). Identifying and Predicting intentional self-harm in electronic health record clinical notes: Deep learning approach. JMIR Medical Informatics, 8(7), e17784.

    Article  Google Scholar 

  36. Huang, S. C., Pareek, A., Zamanian, R., Banerjee, I., & Lungren, M. P. (2020). Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: A case-study in pulmonary embolism detection. Scientific Reports, 10(1), 1–9.

    Article  Google Scholar 

  37. Chassagnon, G., Vakalopolou, M., Paragios, N., & Revel, M. P. (2020). Deep learning: Definition and perspectives for thoracic imaging. European radiology, 30(4), 2021–2030.

    Article  Google Scholar 

  38. Xu, Y., et al. (2019). Deep learning predicts lung cancer treatment response from serial medical imaging. Clinical Cancer Research, 25(11), 3266–3275.

    Article  Google Scholar 

  39. Ding, Y., et al. (2019). A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology, 290(2), 456–464.

    Article  Google Scholar 

  40. Lecouat, B., et al. (2018). Semi-supervised deep learning for abnormality classification in retinal images. arXiv preprint arXiv:1812.07832.

  41. Mobadersany, P., et al. (2018). Predicting cancer outcomes from histology and genomics using convolutional networks. Proceedings of the National Academy of Sciences, 115(13), E2970–E2979.

    Article  Google Scholar 

  42. Chen, Z., He, N., Huang, Y., Qin, W. T., Liu, X., & Li, L. (2018). Integration of a deep learning classifier with a random forest approach for predicting malonylation sites. Genomics, Proteomics & Bioinformatics, 16(6), 451–459.

    Article  Google Scholar 

  43. Xie, R., Wen, J., Quitadamo, A., Cheng, J., & Shi, X. (2017). A deep auto-encoder model for gene expression prediction. BMC Genomics, 18(9), 39–49.

    Google Scholar 

  44. Feldman, K., Faust, L., Wu, X., Huang, C., & Chawla, N. V. (2017). Beyond volume: The impact of complex healthcare data on the machine learning pipeline. In Towards integrative machine learning and knowledge extraction (pp. 150–169). Springer.

    Google Scholar 

  45. Mavrogiorgou, A., Kiourtis, A., Perakis, K., Miltiadou, D., Pitsios, S., & Kyriazis, D. (2019). Analyzing data and data sources towards a unified approach for ensuring end-to-end data and data sources quality in healthcare 4.0. Computer Methods and Programs in Biomedicine, 181, 104967.

    Google Scholar 

  46. Kumar, P. M., Hong, C. S., Afghah, F., Manogaran, G., Yu, K., Hua, Q., & Gao, J. (2021). Clouds proportionate medical data stream analytics for internet of things-based healthcare systems. IEEE Journal of Biomedical and Health Informatics.

    Google Scholar 

  47. Ghadiyaram, D., Pan, J., & Bovik, A. C. (2018). Learning a continuous-time streaming video QoE model. IEEE Transactions on Image Processing, 27(5), 2257–2271.

    Article  MathSciNet  MATH  Google Scholar 

  48. Chen, F., Liu, L., Tang, B., Chen, B., Xiao, W., & Zhang, F. (2021). A novel fusion approach of deep convolution neural network with auto-encoder and its application in planetary gearbox fault diagnosis. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 235(1), 3–16.

    Google Scholar 

  49. Laczik, T. (2021). Encoding Temporal healthcare data for machine learning.

    Google Scholar 

  50. Silsand, L., & Christensen, B. (2017). Generification in change: the complexity of modelling the healthcare domain. In Infrahealth 2017-Proceedings of the 6th International Workshop on Infrastructure in Healthcare 2017.

    Google Scholar 

  51. ElShawi, R., Sherif, Y., Al‐Mallah, M., & Sakr, S. (2020). Interpretability in healthcare: A comparative study of local machine learning interpretability techniques. Computational Intelligence.

    Google Scholar 

  52. Zhou, X., Liang, W., Kevin, I., Wang, K., Wang, H., Yang, L. T., & Jin, Q. (2020). Deep-learning-enhanced human activity recognition for Internet of healthcare things. IEEE Internet of Things Journal, 7(7), 6429–6438.

    Article  Google Scholar 

  53. Castiglioni, I., et al. (2021). AI applications to medical images: From machine learning to deep learning. Physica Medica, 83, 9–24.

    Article  Google Scholar 

  54. Zerka, F. (2020). Systematic review of privacy-preserving distributed machine learning from federated databases in health care. JCO Clinical Cancer Informatics, 4, 184–200.

    Google Scholar 

  55. Hong, S., Zhou, Y., Shang, J., Xiao, C., & Sun, J. (2020). Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Computers in Biology and Medicine, 122, 103801.

    Article  Google Scholar 

  56. Pham, T., Tran, T., Phung, D., & Venkatesh, S. (2017). Predicting healthcare trajectories from medical records: A deep learning approach. Journal of Biomedical Informatics, 69, 218–229.

    Article  Google Scholar 

  57. Rahman, M. A., Hossain, M. S., Alrajeh, N. A., & Guizani, N. (2020). B5G and explainable deep learning assisted healthcare vertical at the edge: COVID-I9 perspective. IEEE Network, 34(4), 98–105.

    Article  Google Scholar 

  58. Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., & Acharya, U. R. (2018). Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine, 161, 1–13.

    Article  Google Scholar 

  59. San Kim, T., & Sohn, S. Y. (2020). Multitask learning for health condition identification and remaining useful life prediction: deep convolutional neural network approach. Journal of Intelligent Manufacturing, 1–11.

    Google Scholar 

  60. Sen, D., Aghazadeh, A., Mousavi, A., Nagarajaiah, S., Baraniuk, R., & Dabak, A. (2019). Data-driven semi-supervised and supervised learning algorithms for health monitoring of pipes. Mechanical Systems and Signal Processing, 131, 524–537.

    Article  Google Scholar 

  61. Santosh, K. C. (2020). AI-driven tools for coronavirus outbreak: Need of active learning and cross-population train/test models on multitudinal/multimodal data. Journal of Medical Systems, 44(5), 1–5.

    Article  Google Scholar 

  62. Nagasubramanian, G., & Sankayya, M. (2021). Multi-variate vocal data analysis for detection of Parkinson disease using deep learning. Neural Computing and Applications, 33(10), 4849–4864.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

    Jyotismita Chaki

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  1. Jyotismita Chaki
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Correspondence to Jyotismita Chaki .

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Editors and Affiliations

  1. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    B. K. Tripathy

  2. Department of Mathematics and Computer Science, St. Mary’s University, Halifax, NS, Canada

    Pawan Lingras

  3. Bharti School of Telecommunications Technology and Management, Indian Institute of Technology, New Delhi, India

    Arpan Kumar Kar

  4. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    Chiranji Lal Chowdhary

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Chaki, J. (2022). Deep Learning in Healthcare: Applications, Challenges, and Opportunities. In: Tripathy, B.K., Lingras, P., Kar, A.K., Chowdhary, C.L. (eds) Next Generation Healthcare Informatics. Studies in Computational Intelligence, vol 1039. Springer, Singapore. https://doi.org/10.1007/978-981-19-2416-3_2

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