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Building a Digital Health Twin for Personalized Intervention: The EPI Project

Authors Jamila Alsayed Kassem , Corinne Allaart, Saba Amiri, Milen Kebede, Tim Müller, Rosanne Turner, Adam Belloum, L. Thomas van Binsbergen , Peter Grunwald, Aart van Halteren, Paola Grosso, Cees de Laat, Sander Klous

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Author Details

Jamila Alsayed Kassem
  • MNS, University of Amsterdam, The Netherlands
Corinne Allaart
  • Vrije Universiteit Amsterdam, The Netherlands
  • St. Antonius Ziekenhuis, The Netherlands
Saba Amiri
  • MNS, University of Amsterdam, The Netherlands
Milen Kebede
  • CCI, University of Amsterdam, The Netherlands
Tim Müller
  • CCI, University of Amsterdam, The Netherlands
Rosanne Turner
  • CWI, Amsterdam, The Netherlands
  • UMC Utrecht, The Netherlands
Adam Belloum
  • MNS, University of Amsterdam, The Netherlands
L. Thomas van Binsbergen
  • CCI, University of Amsterdam, The Netherlands
Peter Grunwald
  • CWI, Amsterdam, The Netherlands
  • Leiden University, The Netherlands
Aart van Halteren
  • Vrije Universiteit Amsterdam, The Netherlands
  • Philips Research, Eindhoven, The Netherlands
Paola Grosso
  • MNS, University of Amsterdam, The Netherlands
Cees de Laat
  • CCI, University of Amsterdam, The Netherlands
Sander Klous
  • University of Amsterdam, The Netherlands
  • KPMG, Netherlands

Funding

The EPI project is funded by the Dutch Science Foundation in the Commit2Data program (grant no: 628.011.028).

Cite AsGet BibTex

Jamila Alsayed Kassem, Corinne Allaart, Saba Amiri, Milen Kebede, Tim Müller, Rosanne Turner, Adam Belloum, L. Thomas van Binsbergen, Peter Grunwald, Aart van Halteren, Paola Grosso, Cees de Laat, and Sander Klous. Building a Digital Health Twin for Personalized Intervention: The EPI Project. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 2:1-2:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/OASIcs.Commit2Data.2

Abstract

The Enabling Personalized Interventions (EPI) project, part of the COMMIT2DATA top sector initiative, brings together research on data science, software-defined network infrastructure, and secure and trustworthy data sharing, executed within the healthcare domain. The project applies the digital twin paradigm, in which data science-driven algorithms monitor and perform functions on a digital counterpart of a real-world entity, to enable proactive responses based on predicted outcomes. The EPI project applies this paradigm in the healthcare context by developing and testing applications that can act as personalized digital health twins for self/-joint management. The EPI project addresses several challenges to digital twin applications in the healthcare domain, such as: 1) strict health data sharing policies often lead to data being locked in silos, 2) legal, policy and privacy requirements make data processing increasingly more complex, and 3) significant limitations on infrastructure resources may apply. In this paper, we report on the use cases the EPI used as the basis to develop possible solutions to these challenges. In particular, we describe algorithms and tools for algorithmic real-time response and analysis of distributed data at scale. We discuss the automatic enforcement of legal interpretations and data-sharing conditions as executable policies. Finally, we investigate infrastructural challenges by implementing and experimenting with the EPI Framework - consisting of a distributed analysis infrastructure and BRANE for orchestrating multi-site applications. We conclude by describing our Proof of Concept (PoC) and showing its application to one of the EPI use cases.

Subject Classification

ACM Subject Classification
  • Information systems → Data exchange
Keywords
  • Healthcare
  • Data Sharing
  • Personalised Medicine
  • Real-time Data Analysis
  • Digital Health Twin
  • Data Policies

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References

  1. Sushant Agarwal, Simon Steyskal, Franjo Antunovic, and Sabrina Kirrane. Legislative compliance assessment: framework, model and gdpr instantiation. In Privacy Technologies and Policy: 6th Annual Privacy Forum, APF 2018, Barcelona, Spain, June 13-14, 2018, Revised Selected Papers 6, pages 131-149. Springer, 2018. URL: https://doi.org/10.1007/978-3-030-02547-2_8.
  2. Corinne G. Allaart, Björn Keyser, Henri Bal, and Aart van Halteren. Vertical split learning - an exploration of predictive performance in medical and other use cases. In 2022 International Joint Conference on Neural Networks (IJCNN), pages 1-8, 2022. URL: https://doi.org/10.1109/IJCNN55064.2022.9891964.
  3. Joshua Baugh, Ute Bartels, James Leach, Blaise Jones, Brooklyn Chaney, Katherine E Warren, Jenavieve Kirkendall, Renee Doughman, Cynthia Hawkins, Lili Miles, et al. The international diffuse intrinsic pontine glioma registry: an infrastructure to accelerate collaborative research for an orphan disease. Journal of neuro-oncology, 132(2):323-331, 2017. Google Scholar
  4. Giovanni Briganti, Marco Scutari, and Richard J McNally. A tutorial on bayesian networks for psychopathology researchers. Psychological methods, 2022. Google Scholar
  5. Koen Bruynseels, Filippo Santoni de Sio, and Jeroen van den Hoven. Digital twins in health care: Ethical implications of an emerging engineering paradigm. Frontiers in Genetics, 9, 2018. URL: https://doi.org/10.3389/fgene.2018.00031.
  6. Iker Ceballos, Vivek Sharma, Eduardo Mugica, Abhishek Singh, Alberto Roman, Praneeth Vepakomma, and Ramesh Raskar. Splitnn-driven vertical partitioning. arXiv preprint arXiv:2008.04137, 2020. URL: https://arxiv.org/abs/2008.04137.
  7. Council of the EU. General Data Protection Regulation. Official Journal of the European Union, 59, 2016. Google Scholar
  8. Ewa Deelman, Karan Vahi, Mats Rynge, Rajiv Mayani, Rafael Ferreira da Silva, George Papadimitriou, and Miron Livny. The evolution of the pegasus workflow management software. Computing in Science & Engineering, 21(4):22-36, 2019. URL: https://doi.org/10.1109/MCSE.2019.2919690.
  9. Vanessa Díaz-Zuccarini and Silvia Schievano. Biomedical imaging and computational modeling in cardiovascular disease: Patient-specific applications using numerical models. Biomedical Imaging and Computational Modeling in Biomechanics, pages 173-192, 2013. Google Scholar
  10. Celtia Domínguez-Fernández, June Egiguren-Ortiz, Jone Razquin, Margarita Gómez-Galán, Laura De las Heras-García, Elena Paredes-Rodríguez, Egoitz Astigarraga, Cristina Miguélez, and Gabriel Barreda-Gómez. Review of technological challenges in personalised medicine and early diagnosis of neurodegenerative disorders. International Journal of Molecular Sciences, 24(4), 2023. URL: https://doi.org/10.3390/ijms24043321.
  11. Filip Karlo Došilović, Mario Brčić, and Nikica Hlupić. Explainable artificial intelligence: A survey. In 2018 41st International convention on information and communication technology, electronics and microelectronics (MIPRO), pages 0210-0215. IEEE, 2018. Google Scholar
  12. Glyn Elwyn, Dominick Frosch, Richard Thomson, Natalie Joseph-Williams, Amy Lloyd, Paul Kinnersley, Emma Cording, Dave Tomson, Carole Dodd, Stephen Rollnick, et al. Shared decision making: a model for clinical practice. Journal of general internal medicine, 27:1361-1367, 2012. Google Scholar
  13. Christopher A. Esterhuyse, Tim Müller, L. Thomas Van Binsbergen, and Adam S. Z. Belloum. Exploring the enforcement of private, dynamic policies on medical workflow execution. In 2022 IEEE 18th International Conference on e-Science (e-Science), pages 481-486, 2022. URL: https://doi.org/10.1109/eScience55777.2022.00086.
  14. Peter Grünwald. Beyond neyman-pearson. arXiv preprint arXiv:2205.00901, 2022. Google Scholar
  15. Qijian He, Wei Yang, Bingren Chen, Yangyang Geng, and Liusheng Huang. Transnet: Training privacy-preserving neural network over transformed layer. Proceedings of the VLDB Endowment, 13(12):1849-1862, 2020. URL: http://www.vldb.org/pvldb/vol13/p1849-he.pdf.
  16. Lindsey M Hoffman, Sophie EM Veldhuijzen Van Zanten, Niclas Colditz, Joshua Baugh, Brooklyn Chaney, Marion Hoffmann, Adam Lane, Christine Fuller, Lili Miles, Cynthia Hawkins, et al. Clinical, radiologic, pathologic, and molecular characteristics of long-term survivors of diffuse intrinsic pontine glioma (dipg): a collaborative report from the international and european society for pediatric oncology dipg registries. Journal of clinical oncology, 36(19):1963, 2018. Google Scholar
  17. Steven R Howard, Aaditya Ramdas, Jon McAuliffe, and Jasjeet Sekhon. Time-uniform, nonparametric, nonasymptotic confidence sequences. The Annals of Statistics, 49(2), 2021. Google Scholar
  18. Renato Iannella and Serena Villata. Odrl information model 2.2. W3C Recommendation, 15, 2018. Google Scholar
  19. MHA Jansen, DG Van Vuurden, WP Vandertop, and GJL Kaspers. Diffuse intrinsic pontine gliomas: a systematic update on clinical trials and biology. Cancer treatment reviews, 38(1):27-35, 2012. Google Scholar
  20. Changqing Ji, Yu Li, Wenming Qiu, Uchechukwu Awada, and Keqiu Li. Big data processing in cloud computing environments. In 2012 12th international symposium on pervasive systems, algorithms and networks, pages 17-23. IEEE, 2012. Google Scholar
  21. Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Advances and open problems in federated learning. Foundations and Trendsregistered in Machine Learning, 14(1-2):1-210, 2021. URL: https://doi.org/10.1561/2200000083.
  22. Jamila Alsayed Kassem, Adam Belloum, Tim Müller, and Paola Grosso. Utilisation profiles of bridging function chain for healthcare use cases. In 2022 IEEE 18th International Conference on e-Science (e-Science), pages 475-480, 2022. URL: https://doi.org/10.1109/eScience55777.2022.00085.
  23. Jamila Alsayed Kassem, Cees De Laat, Arie Taal, and Paola Grosso. The epi framework: A dynamic data sharing framework for healthcare use cases. IEEE Access, 8:179909-179920, 2020. URL: https://doi.org/10.1109/ACCESS.2020.3028051.
  24. Jamila Alsayed Kassem, Onno Valkering, Adam Belloum, and Paola Grosso. Epi framework: Approach for traffic redirection through containerised network functions. In 2021 IEEE 17th International Conference on eScience (eScience), pages 80-89, 2021. URL: https://doi.org/10.1109/eScience51609.2021.00018.
  25. Milen G Kebede, Giovanni Sileno, and Tom Van Engers. A critical reflection on odrl. In AI Approaches to the Complexity of Legal Systems XI-XII: AICOL International Workshops 2018 and 2020: AICOL-XI@ JURIX 2018, AICOL-XII@ JURIX 2020, XAILA@ JURIX 2020, Revised Selected Papers XII, pages 48-61. Springer, 2021. URL: https://doi.org/10.1007/978-3-030-89811-3_4.
  26. Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492, 2016. Google Scholar
  27. Shilpa Krishnan, Catherine C Hay, Monique R Pappadis, Anne Deutsch, and Timothy A Reistetter. Stroke survivors' perspectives on post-acute rehabilitation options, goals, satisfaction, and transition to home. Journal of Neurologic Physical Therapy, 43(3):160-167, 2019. Google Scholar
  28. Peter Langhorne, Julie Bernhardt, and Gert Kwakkel. Stroke rehabilitation. The Lancet, 377(9778):1693-1702, 2011. URL: https://doi.org/10.1016/S0140-6736(11)60325-5.
  29. Guido Noordende, Silvia Olabarriaga, Matthijs Koot, and Laat M. Trusted data management for grid-based medical applications, January 2011. Google Scholar
  30. Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson. Scalable private learning with pate. arXiv preprint arXiv:1802.08908, 2018. URL: https://arxiv.org/abs/1802.08908.
  31. Wullianallur Raghupathi and Viju Raghupathi. Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1):3, 2014. URL: https://doi.org/10.1186/2047-2501-2-3.
  32. Aaditya Ramdas, Peter Grünwald, Vladimir Vovk, and Glenn Shafer. Game-theoretic statistics and safe anytime-valid inference. arXiv preprint arXiv:2210.01948, 2022. URL: https://doi.org/10.48550/arXiv.2210.01948.
  33. OASIS Standard. extensible access control markup language (xacml) version 3.0. A:(22 January 2013). URl: http://docs. oasis-open. org/xacml/3.0/xacml-3.0-core-spec-os-en. html, 2013. Google Scholar
  34. Judith ter Schure and Peter Grünwald. Accumulation bias in meta-analysis: the need to consider time in error control. F1000Research, 8, 2019. Google Scholar
  35. Rosanne J. Turner, Femke Coenen, Femke Roelofs, Karin Hagoort, Aki Härmä, Peter D. Grünwald, Fleur P. Velders, and Floortje E. Scheepers. Information extraction from free text for aiding transdiagnostic psychiatry: constructing nlp pipelines tailored to clinicians’ needs. BMC psychiatry, 22(1):407, 2022. Google Scholar
  36. Rosanne J. Turner and Peter D. Grünwald. Safe sequential testing and effect estimation in stratified count data. arXiv preprint arXiv:2302.11401, 2023. Google Scholar
  37. Rosanne J. Turner and Peter D. Grünwald. Exact anytime-valid confidence intervals for contingency tables and beyond. Statistics & Probability Letters, page 109835, 2023. URL: https://doi.org/10.1016/j.spl.2023.109835.
  38. Onno Valkering, Reginald Cushing, and A. Belloum. Brane: A framework for programmable orchestration of multi-site applications. In 17th IEEE International Conference on eScience, eScience 2021, Innsbruck, Austria, September 20-23, 2021, pages 277-282, September 2021. URL: https://doi.org/10.1109/eScience51609.2021.00056.
  39. L Thomas van Binsbergen, Milen G Kebede, Joshua Baugh, Tom Van Engers, and Dannis G van Vuurden. Dynamic generation of access control policies from social policies. Procedia Computer Science, 198:140-147, 2022. URL: https://doi.org/10.1016/J.PROCS.2021.12.221.
  40. L. Thomas van Binsbergen, Lu-Chi Liu, Robert van Doesburg, and Tom van Engers. Eflint: A domain-specific language for executable norm specifications. In Proceedings of the 19th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences, GPCE 2020, pages 124-136, New York, NY, USA, 2020. Association for Computing Machinery. URL: https://doi.org/10.1145/3425898.3426958.
  41. Lourens E. Veen, Sara Shakeri, and Paola Grosso. Mahiru: a federated, policy-driven data processing and exchange system, 2022. https://arxiv.org/abs/2210.17155, URL: https://doi.org/10.48550/arXiv.2210.17155.
  42. R. Venkateswaran. Virtual private networks. IEEE Potentials, 20(1):11-15, 2001. URL: https://doi.org/10.1109/45.913204.
  43. Eric-Jan Wagenmakers. A practical solution to the pervasive problems of p values. Psychonomic bulletin & review, 14(5):779-804, 2007. Google Scholar
  44. Qingchen Zhang, Laurence T. Yang, Zhikui Chen, and Peng Li. A survey on deep learning for big data. Information Fusion, 42:146-157, 2018. URL: https://doi.org/10.1016/j.inffus.2017.10.006.
  45. Tianwei Zhang, Zecheng He, and Ruby B Lee. Privacy-preserving machine learning through data obfuscation. arXiv preprint arXiv:1807.01860, 2018. URL: https://arxiv.org/abs/1807.01860.
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