Exploring the potential of deep learning models integrating transformer and LSTM in predicting blood glucose levels for T1D patients
This study demonstrates that the combined Transformer and LSTM model can effectively predict blood glucose concentration in type 1 diabetes patients with high accuracy and clinical applicability. The model provides a promising solution for personalized blood glucose management, contributing to the advancement of artificial intelligence technology in diabetes care.
Authors
- Heng Su (ORCID: https://orcid.org/0000-0002-7742-9193)
- Yunying Cai (ORCID: https://orcid.org/0000-0003-2355-1502)
- XinLiang Yang
- Xin Xiong (ORCID: https://orcid.org/0000-0002-2206-5206)
- Jia He (ORCID: https://orcid.org/0009-0002-4766-6718)
- Yuxin Xue
Institutions
- Kunming University of Science and Technology (CN)
- First People's Hospital of Yunnan Province (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1177/20552076251328980
- Citations
- 5
- Primary Topic
- Diabetes Management and Research
- Type
- article
- Field-Weighted Citation Impact
- 6.47