MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning
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| Publicat a: | arXiv.org (Dec 4, 2024), p. n/a |
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| Autor principal: | |
| Altres autors: | , |
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Cornell University Library, arXiv.org
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| Accés en línia: | Citation/Abstract Full text outside of ProQuest |
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MARC
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|---|---|---|---|
| 001 | 3113848364 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 024 | 7 | |a 10.1145/3708534 |2 doi | |
| 035 | |a 3113848364 | ||
| 045 | 0 | |b d20241204 | |
| 100 | 1 | |a Hassan Sartaj | |
| 245 | 1 | |a MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 4, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Testing healthcare Internet of Things (IoT) applications at system and integration levels necessitates integrating numerous medical devices of various types. 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 OsloCity'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 1000 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 1000 DTs in a scalable manner while maintaining the fidelity level, thus serving in place of physical devices for testing. | |
| 653 | |a Accuracy | ||
| 653 | |a Medical electronics | ||
| 653 | |a Medical equipment | ||
| 653 | |a Medical devices | ||
| 653 | |a Internet of medical things | ||
| 653 | |a Learning | ||
| 653 | |a Health care | ||
| 653 | |a Digital twins | ||
| 653 | |a Context | ||
| 653 | |a Internet of Things | ||
| 700 | 1 | |a Ali, Shaukat | |
| 700 | 1 | |a Gjøby, Julie Marie | |
| 773 | 0 | |t arXiv.org |g (Dec 4, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3113848364/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2410.03585 |