Leveraging deep learning models for continuous glucose monitoring and prediction in diabetes management: towards enhanced blood sugar control

Author:

Mohamed Yousuff A. R.ORCID,Zainulabedin Hasan M.,Anand R.,Rajasekhara Babu M.

Publisher

Springer Science and Business Media LLC

Reference20 articles.

1. Acciaroli G, Vettoretti M, Facchinetti A, Sparacino G (2018) Calibration of minimally invasive continuous glucose monitoring sensors: state-of-the-art and current perspectives. Biosensors 8:24

2. Brew-Sam N, Chhabra M, Parkinson A, Hannan K, Brown E, Ped-ley L, Brown K, Wright K, Pedley E, Nolan CJ et al (2021) Experiences of young people and their caregivers of using technology to manage type 1 diabetes mellitus: systematic literature review and narrative synthesis. J Med Internet Res Diabetes 6(1):e20973

3. Chen J, Li K, Herrero P, Zhu T, Georgiou P (2018) Dilated recurrent neural network for short-time prediction of glucose concentration. In: 3rd International workshop on knowledge discovery in healthcare data, KDH@ ICML/IJCAI 2018, 2018, pp 69–73

4. Cui R, Hettiarachchi C, Nolan CJ, Daskalaki E, Suominen H (2021) Personalised short-term glucose prediction via recurrent self-attention network. In: 2021 IEEE 34th international symposium on computer-based medical systems (CBMS), Aveiro, Portugal, 2021, pp 154–159.https://doi.org/10.1109/CBMS52027.2021.00064

5. El Idrissi T, Idri A, Bakkoury Z (2019) Systematic map and review of predictive techniques in diabetes self-management. Int J Inf Manage 46:263–277

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1. AI in Healthcare;Advances in Systems Analysis, Software Engineering, and High Performance Computing;2024-05-14

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