Generalized information with examples on the possibility of using a service-oriented approach and artificial intelligence technologies in the industry of e-Health

Authors

DOI:

https://doi.org/10.15587/2706-5448.2023.285935

Keywords:

service-oriented approach, weak link, web services, artificial intelligence, machine learning, body sensors, remote monitoring

Abstract

The object of the research is the review of ways of implementing service-oriented approaches (SOA) and artificial intelligence (AI) technologies in modern healthcare systems. The generalization of these ways will allow to cope with complex modern challenges, such as increasing demand for medical services, growing volumes of data, and the need for high-quality and effective treatment. This work is aimed at this.

The field of e-Health is rapidly gaining popularity and combines many different systems. But due to the large number of tools and system providers with different architectures, there is a problem that different systems are difficult or impossible to integrate and connect with each other.

It is shown that the use of SOA makes it possible to break down complex systems into separate services that can interact with each other to ensure fast and accurate data processing, effective management of medical resources, and improvement of the quality of medical services. AI can be used to analyze large volumes of medical data, predict risks, diagnose diseases, and develop individualized treatment plans. The use of AI in healthcare systems helps improve diagnostic accuracy, reduce treatment times, and improve patient outcomes. The synergy of SOA and AI in health care systems is important when SOA provides the means to integrate various AI solutions, which allows for the interaction of different services and the exchange of data to ensure effective treatment and collaboration between medical professionals and artificial intelligence systems. Such distribution of systems makes it possible to scale them without affecting other services that are already running. Therefore, it becomes possible to use unified data transfer protocols and combine different services into one system without radically changing the codebase and building additional layers of abstraction for interaction between services that cannot be combined in one system. Examples of the use of SOA and AI in modern health care systems to improve the quality of medical services, optimize resources and ensure an individual and effective approach to patient treatment, which can be used at the next stages of medical reform in Ukraine, are considered.

Author Biographies

Anatolii Petrenko, National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»

Doctor of Technical Science, Professor

Department of System Design

Oleh Boloban, National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»

Postgraduate Student

Department of System Design

References

  1. Božić, V. (2023). Service Oriented Architecture & Smart hospital. Available at: https://www.researchgate.net/publication/367965926_Service_Oriented_Architecture_Smart_hospital
  2. Petrenko, A. I. (2022). Medychna diahnostyka zdorov’ia na domu yak servis. Klinichna informatyka i telemedytsyna, 16 (17).
  3. Petrenko, F. S., Petrenko, O. O. (2023). Wireless sensor networks for healthcare on SOA, Studies in Computational Intelligence series: System Analysis & Intelligent Computing: Theory and Applications. Springer Nature.
  4. Memorandum shchodo namiriv spivpratsi u pobudovy v Ukraini prozoroi ta efektyvnoi elektronnoi systemy okhorony zdorov’ia (2016). MOZ Ukrainy 02.11.2016.
  5. Onawole, H. (2018). MediaTek Sensio MT6381 Biosensor Announced: World’s First 6-in-1 Health Monitoring Module For Smartphones. Available at: https://www.gizmochina.com/2018/01/18/mediatek-sensio-mt6381-biosensor-announced-worlds-first-6-1-health-monitoring-module-smartphones/
  6. De Capua, C., Meduri, A., Morello, R. (2010). A Smart ECG Measurement System Based on Web-Service-Oriented Architecture for Telemedicine Applications. IEEE Transactions on Instrumentation and Measurement, 59 (10), 2530–2538. doi: https://doi.org/10.1109/tim.2010.2057652
  7. Immanuel, V. A., Raj, P. (2015). Enabling context-awareness: A service oriented architecture implementation for a hospital use case. 2015 International Conference on Applied and Theoretical Computing and Communication Technology (ICATccT), 224–228. doi: https://doi.org/10.1109/icatcct.2015.7456886
  8. Hsieh, S.-H., Hsieh, S.-L., Cheng, P.-H., Lai, F. (2012). E-Health and Healthcare Enterprise Information System Leveraging Service-Oriented Architecture. Telemedicine and E-Health, 18 (3), 205–212. doi: https://doi.org/10.1089/tmj.2011.0100
  9. Omar, W. M., Taleb-Bendiab, A. (2006). E-health support services based on service-oriented architecture. IT Professional, 8 (2), 35–41. doi: https://doi.org/10.1109/mitp.2006.32
  10. Cloud Computing Services – Amazon Web Services (AWS). Available at: https://aws.amazon.com/
  11. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S. et al. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2 (4), 230–243. doi: https://doi.org/10.1136/svn-2017-000101
  12. Lopez-Jimenez, F., Attia, Z., Arruda-Olson, A. M., Carter, R., Chareonthaitawee, P., Jouni, H. et al. (2020). Artificial Intelligence in Cardiology: Present and Future. Mayo Clinic Proceedings, 95 (5), 1015–1039. doi: https://doi.org/10.1016/j.mayocp.2020.01.038
  13. Darcy, A. M., Louie, A. K., Roberts, L. W. (2016). Machine Learning and the Profession of Medicine. JAMA, 315 (6), 551–552. doi: https://doi.org/10.1001/jama.2015.18421
  14. Murff, H. J., FitzHenry, F., Matheny, M. E., Gentry, N., Kotter, K. L., Crimin, K. et al. (2011). Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing. JAMA, 306 (8). doi: https://doi.org/10.1001/jama.2011.1204
  15. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K. et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25 (1), 24–29. doi: https://doi.org/10.1038/s41591-018-0316-z
  16. Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. Cambridge: The MIT Press.
  17. Miotto, R., Li, L., Kidd, B. A., Dudley, J. T. (2016). Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Scientific Reports, 6 (1). doi: https://doi.org/10.1038/srep26094
  18. Jagannatha, A. N., Yu, H. (2016). Structured prediction models for RNN based sequence labeling in clinical text. Proc Conf Empir Methods Nat Lang Process, 856–865. doi: https://doi.org/10.18653/v1/d16-1082
  19. Jagannatha, A. N., Yu, H. (2016). Bidirectional RNN for medical event detection in electronic health records. Proc Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. San Diego, 473–482. doi: https://doi.org/10.18653/v1/n16-1056
  20. Shickel, B., Tighe, P. J., Bihorac, A., Rashidi, P. (2018). Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. IEEE Journal of Biomedical and Health Informatics, 22 (5), 1589–1604. doi: https://doi.org/10.1109/jbhi.2017.2767063
  21. Meiliana, A., Dewi, N. M., Wijaya, A. (2019). Artificial Intelligent in Healthcare. The Indonesian Biomedical Journal, 11 (2), 125–135. doi: https://doi.org/10.18585/inabj.v11i2.844
  22. Beltrami, E. J., Brown, A. C., Salmon, P. J. M., Leffell, D. J., Ko, J. M., Grant-Kels, J. M. (2022). Artificial intelligence in the detection of skin cancer. Journal of the American Academy of Dermatology, 87 (6), 1336–1342. doi: https://doi.org/10.1016/j.jaad.2022.08.028
  23. Petrenko, A., Kyslyi, R., Pysmennyi, I. (2018). Designing security of personal data in distributed health care platform. Technology Audit and Production Reserves, 4 (2 (42)), 10–15. doi: https://doi.org/10.15587/2312-8372.2018.141299
  24. Petrenko, A., Kyslyi, R., Pysmennyi, I. (2018). Detection of human respiration patterns using deep convolution neural networks. Eastern-European Journal of Enterprise Technologies, 4 (9 (94)), 6–13. doi: https://doi.org/10.15587/1729-4061.2018.139997
  25. Kaur, J. (2023). Generative AI in Healthcare and its Uses. Complete Guide. Available at: https://www.xenonstack.com/blog/generative-ai-healthcare
  26. Gangoly, K. (2023). The Role of Generative AI in Healthcare. Available at: https://www.mantralabsglobal.com/the-role-of-generative-ai-in-healthcare/
Generalized information with examples on the possibility of using a service-oriented approach and artificial intelligence technologies in the industry of e-Health

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Published

2023-08-17

How to Cite

Petrenko, A., & Boloban, O. (2023). Generalized information with examples on the possibility of using a service-oriented approach and artificial intelligence technologies in the industry of e-Health. Technology Audit and Production Reserves, 4(2(72), 10–17. https://doi.org/10.15587/2706-5448.2023.285935

Issue

Vol. 4 No. 2(72) (2023): Information and control systems

Section

Information Technologies

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Copyright (c) 2023 Anatolii Petrenko, Oleh Boloban

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