| Autor: |
Ruochen Huang, Wei Feng, Shan Lu, Tao shan, Changwei Zhang, Yun Liu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Digital Communications and Networks, Vol 10, Iss 1, Pp 75-82 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2352-8648 |
| DOI: |
10.1016/j.dcan.2022.09.004 |
| Popis: |
Along with the development of 5G network and Internet of Things technologies, there has been an explosion in personalized healthcare systems. When the 5G and Artificial Intelligence (AI) is introduced into diabetes management architecture, it can increase the efficiency of existing systems and complications of diabetes can be handled more effectively by taking advantage of 5G. In this article, we propose a 5G-based Artificial Intelligence Diabetes Management architecture (AIDM), which can help physicians and patients to manage both acute complications and chronic complications. The AIDM contains five layers: the sensing layer, the transmission layer, the storage layer, the computing layer, and the application layer. We build a test bed for the transmission and application layers. Specifically, we apply a delay-aware RA optimization based on a double-queue model to improve access efficiency in smart hospital wards in the transmission layer. In application layer, we build a prediction model using a deep forest algorithm. Results on real-world data show that our AIDM can enhance the efficiency of diabetes management and improve the screening rate of diabetes as well. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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