Population-Specific Glucose Prediction in Diabetes Care With Transformer-Based Deep Learning on the Edge
Author:
Affiliation:
1. Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, U.K.
2. Institute of Health Informatics, University College London, London, U.K.
Funder
President's Ph.D. Scholarship at Imperial College London
Engineering and Physical Sciences Research Council
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/4156126/10487173/10379014.pdf?arnumber=10379014
Reference57 articles.
1. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045
2. Type 1 diabetes mellitus
3. Technology in the management of type 1 diabetes mellitus — current status and future prospects
4. Type 2 diabetes mellitus
5. Glycaemic control in type 1 diabetes during real time continuous glucose monitoring compared with self monitoring of blood glucose: meta-analysis of randomised controlled trials using individual patient data
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1. BGformer: An improved Informer model to enhance blood glucose prediction;Journal of Biomedical Informatics;2024-09
2. Group-informed attentive framework for enhanced diabetes mellitus progression prediction;Frontiers in Endocrinology;2024-06-24
3. Development of an Explainable Deep Learning-Based Decision Support System for Blood Glucose Levels Forecasting in Type 1 Diabetes Using Edge Computing;IFMBE Proceedings;2024
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