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DC Field | Value | Language |
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dc.contributor.author | Chen, J | - |
dc.contributor.author | Chen, Y | - |
dc.contributor.author | Li, J | - |
dc.contributor.author | Wang, J | - |
dc.contributor.author | Lin, Z | - |
dc.contributor.author | Nandi, AK | - |
dc.date.accessioned | 2021-06-19T05:12:12Z | - |
dc.date.available | 2021-06-19T05:12:12Z | - |
dc.date.issued | 2021-06-11 | - |
dc.identifier | ORCID iDs: Jie Chen https://orcid.org/0000-0002-9811-1694; Jianqiang Li https://orcid.org/0000-0002-2208-962X; Jia Wang https://orcid.org/0000-0003-2308-2259; Asoke K. Nandi https://orcid.org/0000-0001-6248-2875. | - |
dc.identifier.citation | Chen, J. et al. (2022) 'Stroke Risk Prediction with Hybrid Deep Transfer Learning Framework', IEEE Journal of Biomedical and Health Informatics, 26 (1), pp. 411 - 422. doi: 10.1109/jbhi.2021.3088750. | en_US |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/22869 | - |
dc.description.abstract | Copyright © The Author(s) 2021. Stroke has become a leading cause of death and long-term disability in the world, and there is no effective treatment.Deep learning-based approaches have the potential to outperform existing stroke risk prediction models, they rely on large well-labeled data. Due to the strict privacy protection policy in health-care systems, stroke data is usually distributed among different hospitals in small pieces. In addition, the positive and negative instances of such data are extremely imbalanced. Transfer learning solves small data issue by exploiting the knowledge of a correlated domain, especially when multiple source are available.In this work, we propose a novel Hybrid Deep Transfer Learning-based Stroke Risk Prediction (HDTL-SRP) scheme to exploit the knowledge structure from multiple correlated sources (i.e.,external stroke data, chronic diseases data, such as hypertension and diabetes). The proposed framework has been extensively tested in synthetic and real-world scenarios, and it outperforms the state-of-the-art stroke risk prediction models. It also shows the potential of real-world deployment among multiple hospitals aided with 5G/B5G infrastructures. | en_US |
dc.description.sponsorship | National Key R&D Program of China (Grant Number: 2020YFA0908700); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: U1713212, 62072315, 62073225, 61806130, 61836005 and 62006157); Natural Science Foundation of Guangdong Province-Outstanding Youth Program (Grant Number: 2019B151502018); 10.13039/100016691-Guangdong Provincial Pearl River Talents Program (Grant Number: 2019ZT08X603); Technology Research Project of Shenzhen City (Grant Number: JSGG20180507182904693); Public Technology Platform of Shenzhen City (Grant Number: GGFW2018021118145859). | en_US |
dc.format.extent | 411 - 422 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © The Author(s) 2021. Published by Institute of Electrical and Electronics Engineers (IEEE). This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | stroke risk prediction | en_US |
dc.subject | transfer learning | en_US |
dc.subject | generative adversarial networks | en_US |
dc.subject | active learning | en_US |
dc.subject | Bayesian optimization | en_US |
dc.title | Stroke Risk Prediction with Hybrid Deep Transfer Learning Framework | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/jbhi.2021.3088750 | - |
dc.relation.isPartOf | IEEE Journal of Biomedical and Health Informatics | - |
pubs.issue | 1 | - |
pubs.publication-status | Published | - |
pubs.volume | 26 | - |
dc.identifier.eissn | 2168-2208 | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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