Skip to main content

Digital Twin in Healthcare Through the Eyes of the Vitruvian Man

  • Conference paper
  • First Online:
Innovation in Medicine and Healthcare

Abstract

In recent years, worldwide, with the development of technology, a huge amount of data is collected in Electronic Health Records (EHRs). Although vast progress has been made with the use of artificial intelligence in various areas of health domain and for specific problems, it is a fact that to date there is no holistic approach to a patient’s state of health using these technologies. Digital Twin refers to a complete physical and functional description of an item, product, or system, which includes pretty much all the information that could be useful in all—current and next—life cycle phases. This paper presents a platform that, using state of the art technologies such as Microservice Architecture (MSA), containerization (Docker), orchestration (Kubernetes) and Machine Learning Operations (MLOps), whereas it is inspired by Leonardo DaVinci’s Vitruvian man, building the Digital Twin of Patient platform. To achieve that, the platform’s architecture is designed with multiple clusters of Docker containers and Kubernetes orchestration. Specific parts or organs of the human body, are represented by clusters called “digital_twin_components”—DTCs. The set of those DTCs structure the “patient_digital_twin” cluster in which appropriate pipelines define and monitor in real time the “best” possible construction of the patient’s digital twin.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Guo, W., Ge, W., Cui, L., Li, H., Kong, L.: An interpretable disease onset predictive model using crossover attention mechanism from electronic health records. In: IEEE Access 7, 134236–134244 (2019)

    Google Scholar 

  2. Ambrosini, E., Caielli, M., Milis, M., Loizou, C., Azzolino, D., Damanti, S., ... Ferrante, S.: Automatic speech analysis to early detect functional cognitive decline in elderly population. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 212–216. IEEE (2019)

    Google Scholar 

  3. Phung, S., Kumar, A., Kim, J.: A deep learning technique for imputing missing healthcare data. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6513–6516. IEEE (2019)

    Google Scholar 

  4. Carchiolo, V., Longheu, A., Reitano, G., Zagarella, L.: Medical prescription classification: a NLP-based approach. In: 2019 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 605–609. IEEE (2019)

    Google Scholar 

  5. Joshi, N., Tammana, R.: GDALR: an efficient model duplication attack on black box machine learning models. In: 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1–6. IEEE (2019)

    Google Scholar 

  6. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)

    Google Scholar 

  7. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  8. Solares, J.R.A., Raimondi, F.E.D., Zhu, Y., Rahimian, F., Canoy, D., Tran, J., ... Salimi-Khorshidi, G.: Deep learning for electronic health records: a comparative review of multiple deep neural architectures. J. Biomed. Inform. 101, 103337 (2020)

    Google Scholar 

  9. Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13(6), 395–405 (2012)

    Google Scholar 

  10. Huang, S.H., LePendu, P., Iyer, S.V., Tai-Seale, M., Carrell, D., Shah, N.H.: Toward personalizing treatment for depression: predicting diagnosis and severity. J. Am. Med. Inform. Assoc. 21(6), 1069–1075 (2014)

    Article  Google Scholar 

  11. Lyalina, S., Percha, B., LePendu, P., Iyer, S.V., Altman, R.B., Shah, N.H.: Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records. J. Am. Med. Inform. Assoc. 20(e2), e297–e305 (2013)

    Google Scholar 

  12. Wang, X., Sontag, D., Wang, F.: Unsupervised learning of disease progression models. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 85–94 (2014)

    Google Scholar 

  13. Tran, T., Nguyen, T.D., Phung, D., Venkatesh, S.: Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM). J. Biomed. Inform. 54, 96–105 (2015)

    Google Scholar 

  14. Macchi, M., Roda, I., Negri, E., Fumagalli, L.: Exploring the role of digital twin for asset lifecycle management. In: IFAC-PapersOnLine 51(11), 790–795 (2018)

    Google Scholar 

  15. Tao, F., Zhang, H., Liu, A., Nee, A.Y.: Digital twin in industry: state-of-the-art. IEEE Trans. Ind. Inform. 15(4), 2405–2415 (2018)

    Google Scholar 

  16. Bremer, V., Becker, D., Kolovos, S., Funk, B., van Breda, W., Hoogendoorn, M., Riper, H.: Predicting therapy success and costs using baseline characteristics—an approach for personalized treatment recommendations. J. Med. Internet Res. 20(8) (2018)

    Google Scholar 

  17. Bruynseels, K., Santoni de Sio, F., Van den Hoven, J.: Digital twins in health care: ethical implications of an emerging engineering paradigm. Front. Genet. 31 (2018)

    Google Scholar 

  18. Budida, D.A.M., Mangrulkar, R.S.: Design and implementation of smart HealthCare system using IoT. In: 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1–7. IEEE (2017)

    Google Scholar 

  19. Durão, L.F., Haag, S., Anderl, R., Schützer, K., Zancul, E.: Digital twin requirements in the context of industry 4.0. In: IFIP International Conference on Product Lifecycle Management, pp. 204–214. Springer, Cham (2018)

    Google Scholar 

  20. Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W.: Digital twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine 51(11), 1016–1022 (2018)

    Google Scholar 

  21. Liu, Y., Zhang, L., Yang, Y., Zhou, L., Ren, L., Wang, F., Deen, M.J.: A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access 7, 49088–49101 (2019)

    Google Scholar 

  22. Wikipedia—Vitruvian Man. https://en.wikipedia.org/wiki/Vitruvian_Man. Accessed 02 Feb 2022

  23. Bucchiarone, A., Dragoni, N., Dustdar, S., Larsen, S.T., Mazzara, M.: From monolithic to microservices: an experience report from the banking domain. IEEE Softw. 35(3), 50–55 (2018)

    Google Scholar 

  24. Balalaie, A., Heydarnoori, A., Jamshidi, P.: Migrating to cloud-native architectures using microservices: an experience report. In: European Conference on Service-Oriented and Cloud Computing, pp. 201–215. Springer, Cham (2015)

    Google Scholar 

  25. Jamshidi, P., Pahl, C., Mendonça, N.C., Lewis, J., Tilkov, S.: Microservices: the journey so far and challenges ahead. IEEE Softw. 35(3), 24–35 (2018)

    Google Scholar 

  26. Dragoni, N., Lanese, I., Larsen, S.T., Mazzara, M., Mustafin, R., Safina, L.: Microservices: how to make your application scale. In: International Andrei Ershov Memorial Conference on Perspectives of System Informatics, pp. 95–104. Springer, Cham (2017)

    Google Scholar 

  27. Soldani, J., Tamburri, A., Van Den Heuvel, W.J.: The pains and gains of microservices: a systematic grey literature review. J. Syst. Softw. 146, 215–232 (2018)

    Article  Google Scholar 

  28. Santamaria, I., Colomo-Palacios, R., Ebert, C.: Microservices. IEEE Softw. 35(3), 96–100 (2018)

    Google Scholar 

  29. Bila, N., Dettori, P., Kanso, A., Watanabe, Y., Youssef, A.: Leveraging the serverless architecture for securing linux containers. In: 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW), pp. 401–404. IEEE (2017)

    Google Scholar 

  30. AWS—Building applications with serverless architectures. https://aws.amazon.com/lambda/serverless-architectures-learn-more/. Accessed 20 Jan 2022

  31. Rajan, R.A.P.: Serverless architecture-a revolution in cloud computing. In: 2018 Tenth International Conference on Advanced Computing (ICoAC), pp. 88–93. IEEE (2018)

    Google Scholar 

  32. Shah, J., Dubaria, D.: Building modern clouds: using Docker, Kubernetes & Google cloud platform. In: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0184–0189. IEEE (2019)

    Google Scholar 

  33. Docker—Overview. https://docs.docker.com/get-started/overview/. Accessed 20 Dec 2021

  34. Kubernetes. https://kubernetes.io. Accessed 20 Dec 2021

  35. Rossi, F., Cardellini, V., Presti, F.L., Nardelli, M.: Geo-distributed efficient deployment of containers with Kubernetes. Comput. Commun. 159, 161–174 (2020)

    Google Scholar 

  36. Iliashenko, O., Bikkulova, Z., Dubgorn, A.: Opportunities and challenges of artificial intelligence in healthcare. In: E3S Web of Conferences, vol. 110, p. 02028. EDP Sciences (2019)

    Google Scholar 

  37. Xu, P., Shi, S., Chu, X.: Performance evaluation of deep learning tools in Docker containers. In: 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM), pp. 395–403. IEEE (2017)

    Google Scholar 

  38. Huang, Y., Cai, K., Zong, R., Mao, Y.: Design and implementation of an edge computing platform architecture using Docker and Kubernetes for machine learning. In: Proceedings of the 3rd International Conference on High Performance Compilation, Computing and Communications, pp. 29–32 (2019)

    Google Scholar 

  39. Gruendner, J., Schwachhofer, T., Sippl, P., Wolf, N., Erpenbeck, M., Gulden, C., ... Toddenroth, D.: KETOS: clinical decision support and machine learning as a service—a training and deployment platform based on Docker, OMOP-CDM, and FHIR web services. PloS One 14(10), e0223010 (2019)

    Google Scholar 

  40. Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W.: Digital Twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine 51(11). In: 16th IFAC Symposium on Information Control Problems in Manufacturing INCOM, pp. 1016–1022 (2018)

    Google Scholar 

  41. Negri, E., Fumagalli, L., Macchi, M.: A review of the roles of digital twin in CPS-based production systems. Procedia Manuf. 11, 939–948 (2017)

    Article  Google Scholar 

  42. Semeraro, C., Lezoche, M., Panetto, H., Dassisti, M.: Digital twin paradigm: a systematic literature review. Comput. Ind. 130, 103469 (2021)

    Google Scholar 

  43. Grieves, M.: Digital twin: manufacturing excellence through virtual factory replication. White Paper 1, 1–7 (2014)

    Google Scholar 

  44. Rivera, L.F., Jiménez, M., Angara, P., Villegas, N.M., Tamura, G., Müller, H.A.: Towards continuous monitoring in personalized healthcare through digital twins. In: Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering, pp. 329–335 (2019)

    Google Scholar 

  45. Fujii, T.Y., Hayashi, V.T., Arakaki, R., Ruggiero, W.V., Bulla, R., Hayashi, F.H., Khalil, K.A.: A digital twin architecture model applied with MLOps techniques to improve short-term energy consumption prediction. Machines 10(1), 23 (2022)

    Article  Google Scholar 

  46. WHO—“What is health promotion?”. https://www.who.int/news-room/q-a-detail/what-is-health-promotion. Accessed 20 Dec 2021

  47. Elayan, H., Aloqaily, M., Guizani, M.: Digital twin for intelligent context-aware IoT healthcare systems. IEEE Internet Things J. 8(23), 16749–16757 (2021)

    Google Scholar 

  48. Croatti, A., Gabellini, M., Montagna, S., Ricci, A.: On the integration of agents and digital twins in healthcare. J. Med. Syst. 44(9), 1–8 (2020)

    Google Scholar 

  49. Alla, S., Adari, S.K.: Beginning MLOps with MLFlow. Apress (2021)

    Google Scholar 

  50. mongoDB. https://www.mongodb.com/. Accessed 20 Dec 2021

  51. Hyperopt: Distributed asynchronous hyper-parameter optimization. http://hyperopt.github.io/hyperopt. Accessed 20 Dec 2021

  52. Pezoa, F., Reutter, J.L., Suarez, F., Ugarte, M., Vrgoč, D.: Foundations of JSON schema. In: Proceedings of the 25th International Conference on World Wide Web, pp. 263–273 (2016)

    Google Scholar 

  53. Haseeb, A., Pattun, G.: A review on NoSQL: applications and challenges. Int. J. Adv. Res. Comput. Sci. 8(1) (2017)

    Google Scholar 

  54. Raja, P.S., et al.: Missing value imputation using unsupervised machine learning techniques. Soft Comput. 24(6), 4361–4392 (2020)

    Google Scholar 

  55. Li, P., Dai, C., Wang, W.: Inconsistent data cleaning based on the maximum dependency set and attribute correlation. Symmetry 10(10), 516 (2018)

    Google Scholar 

  56. Rhahla, M., Abdellatif, T., Attia, R., Berrayana, W.: A GDPR controller for IoT systems: application to e-health. In: 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 170–173. IEEE (2019)

    Google Scholar 

Download references

Acknowledgements

The research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: BeHEALTHIER - T2EDK-04207).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Spyridon Kleftakis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kleftakis, S., Mavrogiorgou, A., Mavrogiorgos, K., Kiourtis, A., Kyriazis, D. (2022). Digital Twin in Healthcare Through the Eyes of the Vitruvian Man. In: Chen, YW., Tanaka, S., Howlett, R.J., Jain, L.C. (eds) Innovation in Medicine and Healthcare. Smart Innovation, Systems and Technologies, vol 308. Springer, Singapore. https://doi.org/10.1007/978-981-19-3440-7_7

Download citation

Publish with us

Policies and ethics