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MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning

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Název: MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning
Autoři: Hassan Sartaj, Shaukat Ali, Julie Marie Gjøby
Zdroj: ACM Transactions on Software Engineering and Methodology. 34:1-36
Publication Status: Preprint
Informace o vydavateli: Association for Computing Machinery (ACM), 2025.
Rok vydání: 2025
Témata: I.2, Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering, D.2.5, D.2, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology
Popis: Testing healthcare Internet of Things (IoT) applications at system and integration levels necessitates integrating numerous medical devices. Challenges of incorporating medical devices are: (i) their continuous evolution, making it infeasible to include all device variants and (ii) rigorous testing at scale requires multiple devices and their variants, which is time-intensive, costly, and impractical. Our collaborator, Oslo City’s health department, faced these challenges in developing automated test infrastructure, which our research aims to address. In this context, we propose a meta-learning-based approach ( MeDeT ) to generate digital twins (DTs) of medical devices and adapt DTs to evolving devices. We evaluate MeDeT in Oslo City’s context using five widely used medical devices integrated with a real-world healthcare IoT application. Our evaluation assesses MeDeT ’s ability to generate and adapt DTs across various devices and versions using different few-shot methods, the fidelity of these DTs, the scalability of operating 1,000 DTs concurrently, and the associated time costs. Results show that MeDeT can generate DTs with over 96% fidelity, adapt DTs to different devices and newer versions with reduced time cost (around one minute), and operate 1,000 DTs in a scalable manner while maintaining the fidelity level, thus serving in place of physical devices for testing.
Druh dokumentu: Article
Jazyk: English
ISSN: 1557-7392
1049-331X
DOI: 10.1145/3708534
DOI: 10.48550/arxiv.2410.03585
Přístupová URL adresa: http://arxiv.org/abs/2410.03585
Rights: CC BY
Přístupové číslo: edsair.doi.dedup.....81168670459f5a694e9a9c9f49816dec
Databáze: OpenAIRE
Popis
Abstrakt:Testing healthcare Internet of Things (IoT) applications at system and integration levels necessitates integrating numerous medical devices. Challenges of incorporating medical devices are: (i) their continuous evolution, making it infeasible to include all device variants and (ii) rigorous testing at scale requires multiple devices and their variants, which is time-intensive, costly, and impractical. Our collaborator, Oslo City’s health department, faced these challenges in developing automated test infrastructure, which our research aims to address. In this context, we propose a meta-learning-based approach ( MeDeT ) to generate digital twins (DTs) of medical devices and adapt DTs to evolving devices. We evaluate MeDeT in Oslo City’s context using five widely used medical devices integrated with a real-world healthcare IoT application. Our evaluation assesses MeDeT ’s ability to generate and adapt DTs across various devices and versions using different few-shot methods, the fidelity of these DTs, the scalability of operating 1,000 DTs concurrently, and the associated time costs. Results show that MeDeT can generate DTs with over 96% fidelity, adapt DTs to different devices and newer versions with reduced time cost (around one minute), and operate 1,000 DTs in a scalable manner while maintaining the fidelity level, thus serving in place of physical devices for testing.
ISSN:15577392
1049331X
DOI:10.1145/3708534