The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
권호 표지
DOI QR코드

DOI QR Code

IoB Based Scenario Application of Health and Medical AI Platform

보건의료 AI 플랫폼의 IoB 기반 시나리오 적용

  • 임은섭 (호남대학교 대학원 컴퓨터공학과)
  • Received : 2022.10.06
  • Accepted : 2022.12.17
  • Published : 2022.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

At present, several artificial intelligence projects in the healthcare and medical field are competing with each other, and the interfaces between the systems lack unified specifications. Thus, this study presents an artificial intelligence platform for healthcare and medical fields which adopts the deep learning technology to provide algorithms, models and service support for the health and medical enterprise applications. The suggested platform can provide a large number of heterogeneous data processing, intelligent services, model managements, typical application scenarios, and other services for different types of business. In connection with the suggested platform application, we represents a medical service which is corresponding to the trusted and comprehensible tracking and analyzing patient behavior system for Health and Medical treatment using Internet of Behavior concept.

현재 보건의료 분야에서 여러 인공지능 프로젝트가 서로 경쟁하고 있어서 시스템 간 인터페이스의 통일된 사양이 부족한 상황이다. 이에 본 연구에서는 보건의료 부문 관련 응용 알고리즘, 모델 및 서비스 지원을 제공할 수 있는 하나의 보건의료 인공지능 서비스 플랫폼을 제안한다. 제안된 플랫폼은 다수의 이기종 데이터 처리, 지능형 서비스, 모델 관리, 일반 응용 시나리오 및 다양한 수준의 비즈니스를 위한 기타 서비스를 제공할 수 있다. 플랫폼 적용과 관련해서 최근 대두되고 있는 행위 인터넷 개념을 바탕으로 보건의료 분야의 사물 인터넷 서비스 관련 환자 행위 분석을 통해 보건의료 소비 행위에 대해 신뢰할 수 있고, 이해 가능한 추적 및 분석 시나리오를 나타낸다.

Keywords

References

  1. T.B. Murdoch and A.S. Detsky, "The inevitable application of big data to health care.," JAMA, vol. 309, no. 13, 2013, pp. 1351-1352. https://doi.org/10.1001/jama.2013.393
  2. J. He, S.L. Baxter, J. Xu, X. Thou and K. Zhang. "The practical implementation of artificial intelligence technologies in medicine.," Nature Medicine, vol. 25, no. 6, 2019, pp. 30-36. https://doi.org/10.1038/s41591-018-0307-0
  3. D.V.D. Sande. M.E. Genderen, J. Huiskens, and D. Gommers, "Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit," Intensive Care Med, vol. 47, 2021, pp. 750-760. https://doi.org/10.1007/s00134-021-06446-7
  4. D.W. Kim, H.Y. Jang, K.W. Kim, Y. Shin and S.H. Park. "Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers," Korean J. Radiol, vol. 20, 2019, pp. 405-410. https://doi.org/10.3348/kjr.2019.0025
  5. J. Wilkinson, K.F. Arnold, E.J. Murray, M.V. Smeden K. Carr and R. Sippy, "Time to reality check the promises of machine learning-powered precision medicine," Lancet Digit Health, vol. 2, 2020, pp. 677-680.
  6. C.A. Uranus, "A middleware architecture for dependable aal and vital signs monitoring applications.," Sensors, vol. 12, no. 3, 2012, pp. 3145-3161. https://doi.org/10.3390/s120303145
  7. J.M.C. Rodriguez and A. Abraham, "Using heterogeneous wireless sensor networks in a telemonitoring system for healthcare," IEEE Trans. Inf. Technol. Biomed. vol. 14, no. 2, 2010, pp. 234-240. https://doi.org/10.1109/TITB.2009.2034369
  8. A.M. Elmisery, S. Rho and D. Botvich. "A fog based middleware for automated compliance with OECD privacy principlesin internet of healthcare things," IEEE Access., vol. 4, 2016, pp. 8418-8441. https://doi.org/10.1109/ACCESS.2016.2631546
  9. B. Almadani, B. Saeed and A. Alroubaiy, "Healthcare systems integration using real time publish subscribe (RTPS) middleware," Computers and Electrical Engineering, vol. 50, 2016, pp. 67-78. https://doi.org/10.1016/j.compeleceng.2015.12.009
  10. S. Shukla, M.F. Hassan, M.K. Khan, L.T. Jung, and A. Awang, "Ananalytical model to minimize the latency in healthcare internet-of-things in fog computing environment," PLoS One vol. 14, no. 11, 2019, pp. 1-31.
  11. P. Maia, T. Batista, E. Cavalcante, A. Baffa, F.C. Delicato, P.F. Pires, and A. Zomaya, "A web platform for interconnecting body sensors and improving health care," Procedia Computer Science Sci., vol. 40, 2014, pp. 135-142. https://doi.org/10.1016/j.procs.2014.10.041
  12. E.U. Warriach, E. Kaldeli, A. Lazovik and M. Aiello, "An interplatform service-oriented middleware for the smart home.," International Journal of Smart Home., vol. 7, 2013, pp. 115-142. https://doi.org/10.14257/ijsh.2013.7.5.12
  13. P. Bellagente., A. Depari, P. Ferrari, A. Flammini, E. Sisinni, and S. Rinaldi, "M3IoT - Message-oriented middleware for M-health Internet of Things: Design and validation," In IEEE International Instrumentation and Measurement Technology Conf. Houston, TX, USA, 2018.
  14. S. Rab, S. Yadab, and N. Garg, "Evolution of measurement system and SI units in India," MAPAN, vol. 35, no. 5, 2020, pp. 1-16. https://doi.org/10.1007/s12647-020-00369-2
  15. S. Rab, S. Yadav, and A. Haleem, "Quality Infrastructure of National Metrology Institutes: A Comparative Study," Indian Journal of Pure and Applied Physics., Vol. 59, April 2021, pp. 285-303.
  16. G. Fersi, "Middleware for internet of things: a study," Proceedings of IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), Fortaleza, Brazil, 2015, pp. 230-235.
  17. C. Seeger, K.V. Laerhoven, and A. Buchmann, "MyHealthAssistant: an event-driven middleware for multiple medical applications on a smartphone-mediated body sensor network," IEEE Journal of Biomedical and Health Informatics, 19, 2, 2015, 752-760. https://doi.org/10.1109/JBHI.2014.2326604
  18. H. Elayan, M. Aloqaily, F. Karray, and M. Guizani. "Internet of Behavior and Explainable AI Systems for Influencing IoT Behavior," Sensors International. vol. 2, 2021, pp. 2666-3511.
  19. C. Molnar, Interpretable machine learning. A Guide for Making Black Box Models Explainable. Amazon, February 28, 2019.
  20. P. Linardatos, V. Papastefanopoulos, and S. Kotsiantis, "Explainable AI: A Review of Machine Learning Interpretability Methods," Entropy, vol. 23, 2021, pp. 18. https://doi.org/10.3390/e23010018
  21. U. Jamoliddin, K. Ugli, and J. Yoo. "Age Classification, Gender classification, Unfiltered Image Recognition, Imbalanced Classification Problems." J. of the Korea Institute of Electronics Communications Sciences, 2022, vol.17, no.01, pp. 99-104.
  22. H. Lee and W. Cho " Contactless Access Certification Management System for Infection Control in Special Rooms in Medical Institutions." J. of the Korea Institute of Electronics Communications Sciences, 2022, vol.17, no.02, pp. 387-392.
  23. S. Jung and S. Lee, "Adaptive Queue Management Mechanism, Flow Group, Quality of Service, Deep Reinforcement Learning." J. of the Korea Institute of Electronics Communications Sciences, 2020, vol.15, no.06, 1099-1104.