Deep learning for diabetes: a systematic review
Diabetes is a chronic metabolic disorder that affects an estimated 463 million people
worldwide. Aiming to improve the treatment of people with diabetes, digital health has been …
worldwide. Aiming to improve the treatment of people with diabetes, digital health has been …
Minimally invasive electrochemical continuous glucose monitoring sensors: Recent progress and perspective
Diabetes and its complications are seriously threatening the health and well-being of
hundreds of millions of people. Glucose levels are essential indicators of the health …
hundreds of millions of people. Glucose levels are essential indicators of the health …
In–human testing of a non-invasive continuous low–energy microwave glucose sensor with advanced machine learning capabilities
Continuous glucose monitoring schemes that avoid finger pricking are of utmost importance
to enhance the comfort and lifestyle of diabetic patients. To this aim, we propose a …
to enhance the comfort and lifestyle of diabetic patients. To this aim, we propose a …
IoMT-enabled real-time blood glucose prediction with deep learning and edge computing
Blood glucose (BG) prediction is essential to the success of glycemic control in type 1
diabetes (T1D) management. Empowered by the recent development of the Internet of …
diabetes (T1D) management. Empowered by the recent development of the Internet of …
Iot and cloud computing in health-care: A new wearable device and cloud-based deep learning algorithm for monitoring of diabetes
Diabetes is a chronic disease that can affect human health negatively when the glucose
levels in the blood are elevated over the creatin range called hyperglycemia. The current …
levels in the blood are elevated over the creatin range called hyperglycemia. The current …
Few-and single-molecule reservoir computing experimentally demonstrated with surface-enhanced Raman scattering and ion gating
D Nishioka, Y Shingaya, T Tsuchiya, T Higuchi… - Science …, 2024 - science.org
Molecule-based reservoir computing (RC) is promising for achieving low power
consumption neuromorphic computing, although the information-processing capability of …
consumption neuromorphic computing, although the information-processing capability of …
Personalized blood glucose prediction for type 1 diabetes using evidential deep learning and meta-learning
The availability of large amounts of data from continuous glucose monitoring (CGM),
together with the latest advances in deep learning techniques, have opened the door to a …
together with the latest advances in deep learning techniques, have opened the door to a …
Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction–a systematic literature review
Background and aim Hypoglycaemia prediction play an important role in diabetes
management being able to reduce the number of dangerous situations. Thus, it is relevant to …
management being able to reduce the number of dangerous situations. Thus, it is relevant to …
Chinese diabetes datasets for data-driven machine learning
Q Zhao, J Zhu, X Shen, C Lin, Y Zhang, Y Liang, B Cao… - Scientific Data, 2023 - nature.com
Data of the diabetes mellitus patients is essential in the study of diabetes management,
especially when employing the data-driven machine learning methods into the …
especially when employing the data-driven machine learning methods into the …
Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study
WPTM van Doorn, YD Foreman, NC Schaper… - PloS one, 2021 - journals.plos.org
Background Closed-loop insulin delivery systems, which integrate continuous glucose
monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown …
monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown …