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Intelligent Systems in Healthcare: An Architecture Proposal

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Ambient Intelligence – Software and Applications – 14th International Symposium on Ambient Intelligence (ISAmI 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 770))

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Abstract

Multi-Agent Systems has existed for decades and has focused on principles such as loose coupling, distribution, reactivity, and local state. Despite substantial tool and programming language research and development, industry adoption of these systems has been restricted, particularly in the healthcare arena. Artificial intelligence, on the other hand, entails developing computer systems that can execute tasks that normally require human intelligence, such as decision-making, problem-solving, and learning. The goal of this article is to develop and implement an architecture that includes multi-agent systems with microservices, leveraging the capabilities of both methodologies in order to harness the power of Artificial Intelligence in the healthcare industry. It assesses the proposed architecture’s merits and downsides, as well as its relevance to various healthcare use cases and the influence on system scalability, adaptability, and maintainability. Indeed, the proposed architecture is capable of meeting the objectives while maintaining scalability, flexibility, and adaptability.

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References

  1. Cristani, M., Pasetto, L., Tomazzoli, C.: Protecting the environment: a multi-agent approach to environmental monitoring. Proc. Des. Soc. 1, 161–170 (2021)

    Google Scholar 

  2. Benhajji, N., Roy, D., Anciaux, D.: Patient-centered multi agent system for health care. IFAC-PapersOnLine 48(3) (2015)

    Google Scholar 

  3. Munaf, R.M., Ahmed, J., Khakwani, F., Rana, T.: Microservices architecture: challenges and proposed conceptual design. In: 2019 International Conference on Communication Technologies (ComTech), Rawalpindi, Pakistan, 2019, pp. 82–87 (2019). https://doi.org/10.1109/COMTECH.2019.8737831

  4. Francesco, P.D., Malavolta, I., Lago, P.: Research on architecting microservices: trends, focus, and potential for industrial adoption. In: 2017 IEEE International Conference on Software Architecture (ICSA), Gothenburg, Sweden, 2017, pp. 21–30 (2017). https://doi.org/10.1109/ICSA.2017.24

  5. Villamizar, M., et al.: Evaluating the monolithic and the microservice architecture pattern to deploy web applications in the cloud. In: 2015 10th Computing Colombian Conference (10CCC), Bogota, Colombia, 2015, pp. 583–590 (2015). https://doi.org/10.1109/ColumbianCC.2015.7333476

  6. Heorhiadi, V., Rajagopalan, S., Jamjoom, H., Reiter, M.K., Sekar, V.: Gremlin: systematic resilience testing of microservices. In: 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), Nara, Japan, 2016, pp. 57–66 (2016). https://doi.org/10.1109/ICDCS.2016.11

  7. Kang, H., Le, M., Tao, S.: Container and microservice driven design for cloud infrastructure DevOps. In: 2016 IEEE International Conference on Cloud Engineering (IC2E), Berlin, Germany, 2016, pp. 202–211 (2016). https://doi.org/10.1109/IC2E.2016.26

  8. Agrawal, A., Won, S., Sharma, T., Deshpande, M., Mccomb, C.: A multi-agent reinforcement learning framework for intelligent manufacturing with autonomous mobile robots

    Google Scholar 

  9. Radisic-aberger, O., Weisser, T., Sabmannshausen, T., Wagner, J., Burggraf, P.: Concept of a multi-agent system for optimised and automated engineering change implementation. Proc. Des. Soc. 2, 1689–1698 (2022)

    Google Scholar 

  10. Cocho-bermejo, A., Navarro-mateu, D.: User-centered responsive sunlight reorientation system based on multiagent decision-making, UDaMaS. In: Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe, vol. 2, pp. 695–704 (2019)

    Google Scholar 

  11. Calvaresi, D., Albanese, G., Calbimonte, J.-P., Schumacher, M.: SEAMLESS: simulation and analysis for multi-agent system in time-constrained environments. In: Demazeau, Y., Holvoet, T., Corchado, J.M., Costantini, S. (eds.) PAAMS 2020. LNCS (LNAI), vol. 12092, pp. 392–397. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49778-1_30

    Chapter  Google Scholar 

  12. Rehman, H.U., et al.: Cloud based decision making for multi-agent production systems. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds.) EPIA 2021. LNCS (LNAI), vol. 12981, pp. 673–686. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86230-5_53

    Chapter  Google Scholar 

  13. Simmons, A.B., Chappell, S.G.: Artificial intelligence-definition and practice. IEEE J. Oceanic Eng. 13(2), 14–42 (1988). https://doi.org/10.1109/48.551

    Article  Google Scholar 

  14. Helm, J.M., Swiergosz, A.M., Haeberle, H.S., et al.: Machine learning and artificial intelligence: definitions, applications, and future directions. Curr. Rev. Musculoskelet. Med. 13, 69–76 (2020). https://doi.org/10.1007/s12178-020-09600-8

  15. Battineni, G., Sagaro, G.G., Chinatalapudi, N., Amenta, F.: Applications of machine learning predictive models in the chronic disease diagnosis. J. Pers. Med. 10, 21 (2020). https://doi.org/10.3390/jpm10020021

  16. Kowsari, K., et al.: HMIC: hierarchical medical image classification, a deep learning approach. Inf. 11, 318 (2020). https://doi.org/10.3390/info11060318

  17. Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., Soufi, G.: Deep-COVID: predicting COVID-19 from chest X-ray images using deep transfer learning. Med. Image Anal. 65, 101794 (2020). ISSN 1361-8415, https://doi.org/10.1016/j.media.2020.101794, https://www.sciencedirect.com/science/article/pii/S1361841520301584

  18. Shakshuki, E., Reid, M.: Multi-agent system applications in healthcare: current technology and future roadmap. Procedia Comput. Sci. 52 (2015)

    Google Scholar 

  19. Cardoso, L., Marins, F., Portela, F., Santos, M., Abelha, A., Machado, J.: A multi-agent platform for hospital interoperability. In: Ramos, C., Novais, P., Nihan, C.E., Corchado Rodríguez, J.M. (eds.) Ambient Intelligence - Software and Applications. AISC, vol. 291, pp. 127–134. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07596-9_14

    Chapter  Google Scholar 

  20. Collier, R., O’Neill, E., Lillis, D., O’Hare, G.: MAMS: Multi-Agent MicroServices, pp. 655–662 (2019). https://doi.org/10.1145/3308560.3316509

  21. Carneiro, J., Alves, P., Marreiros, G., Novais, P.: A multi-agent system framework for dialogue games in the group decision-making context. Adv. Intell. Syst. Comput. 930, 437–447 (2019)

    Article  Google Scholar 

  22. Zouad, S., Boufaida, M.: Using multi-agent microservices for a better dynamic composition of semantic web services. In: Proceedings of the 4th International Conference on Advances in Artificial Intelligence (ICAAI 20), pp. 47–52. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3441417.3441423

  23. O’Neill, E., Lillis, D., O’Hare, G.M.P., Collier, R.W.: Explicit modelling of resources for multi-agent microservices using the cartago framework. In: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS ’20). International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, pp. 1957–1959 (2020)

    Google Scholar 

  24. Pathirana, S., Asirvatham, D., M’djohar, M.: Applicability of multi-agent systems for electroencephalographic data classification. Procedia Comput. Sci. 152, 36–43 (2019)

    Google Scholar 

  25. Miranda, M., et al.: Multi-agent systems for HL7 interoperability services. Procedia Technol. 5, 725–733 (2012). https://doi.org/10.1016/j.protcy.2012.09.080

    Article  Google Scholar 

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Acknowledgements

This work has been supported by FCT (Fundação para a Ciência e Tecnologia) within the R &D Units Project Scope: UIDB/00319/2020.

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Authors and Affiliations

  1. ALGORITMI/LASI, University of Minho, Braga, Portugal

    António Chaves, Larissa Montenegro, Hugo Peixoto, António Abelha & José Machado

  2. ALGORITMI/LASI, University of Azores, Ponta Delgada, Portugal

    Luís Gomes

Authors
  1. António Chaves
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  2. Larissa Montenegro
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  3. Hugo Peixoto
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  4. António Abelha
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  5. Luís Gomes
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  6. José Machado
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Corresponding author

Correspondence to José Machado .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Paulo Novais

  2. Universitat Politècnica de València, Valencia, Valencia, Spain

    Vicente Julián Inglada

  3. University of Granada, Granada, Spain

    Miguel J. Hornos

  4. National Institute of Informatics, Chiyoda, Japan

    Ichiro Satoh

  5. CIICESI, ESTG, Politécnico do Porto, Felgueiras, Portugal

    Davide Carneiro

  6. ISEP/GECAD, Porto, Portugal

    João Carneiro

  7. Deep tech lab, AIR Institute, Valladolid, Spain

    Ricardo S. Alonso

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Chaves, A., Montenegro, L., Peixoto, H., Abelha, A., Gomes, L., Machado, J. (2023). Intelligent Systems in Healthcare: An Architecture Proposal. In: Novais, P., et al. Ambient Intelligence – Software and Applications – 14th International Symposium on Ambient Intelligence. ISAmI 2023. Lecture Notes in Networks and Systems, vol 770. Springer, Cham. https://doi.org/10.1007/978-3-031-43461-7_23

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