Artificial intelligence and machine learning for improving glycemic control in diabetes: best practices, pitfalls, and opportunities

PG Jacobs, P Herrero, A Facchinetti… - IEEE reviews in …, 2023 - ieeexplore.ieee.org
Objective: Artificial intelligence and machine learning are transforming many fields including
medicine. In diabetes, robust biosensing technologies and automated insulin delivery …

Electrochemical glucose sensors and their applications in diabetes management

A Heller, B Feldman - Chemical reviews, 2008 - ACS Publications
About 6,000 peer reviewed articles have been published on electrochemical glucose assays
and sensors, of which 700 were published in the 2005–2006 two-year period. Their number …

[PDF][PDF] ISPAD Clinical Practice Consensus Guidelines 2018: Glycemic control targets and glucose monitoring for children, adolescents, and young adults with diabetes

LA DiMeglio, CL Acerini, E Codner, ME Craig, SE Hofer… - 2018 - scholarworks.iupui.edu
This is the author's manuscript of the article published in final edited form as: DiMeglio, LA,
Acerini, CL, Codner, E., Craig, ME, Hofer, SE, Pillay, K., & Maahs, DM (2018). ISPAD Clinical …

Convolutional recurrent neural networks for glucose prediction

K Li, J Daniels, C Liu, P Herrero… - IEEE journal of …, 2019 - ieeexplore.ieee.org
Control of blood glucose is essential for diabetes management. Current digital therapeutic
approaches for subjects with type 1 diabetes mellitus such as the artificial pancreas and …

A personalized healthcare monitoring system for diabetic patients by utilizing BLE-based sensors and real-time data processing

G Alfian, M Syafrudin, MF Ijaz, MA Syaekhoni… - Sensors, 2018 - mdpi.com
Current technology provides an efficient way of monitoring the personal health of
individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for …

Assessment and monitoring of glycemic control in children and adolescents with diabetes

MJ Rewers, K Pillay, C De Beaufort, ME Craig… - Pediatric …, 2014 - orbilu.uni.lu
[en] The article presents a chapter of the guidelines on pediatric diabetes in the International
Society for Pediatric and Adolescent Diabetes (ISPAD) Clinical Practice Consensus …

GluNet: A deep learning framework for accurate glucose forecasting

K Li, C Liu, T Zhu, P Herrero… - IEEE journal of …, 2019 - ieeexplore.ieee.org
For people with Type 1 diabetes (T1D), forecasting of blood glucose (BG) can be used to
effectively avoid hyperglycemia, hypoglycemia and associated complications. The latest …

Carbon nanotube fiber-based flexible microelectrode for electrochemical glucose sensors

S Muqaddas, M Javed, S Nadeem, MA Asghar… - ACS …, 2023 - ACS Publications
Electrochemical sensors are gaining significant demand for real-time monitoring of health-
related parameters such as temperature, heart rate, and blood glucose level. A fiber-like …

Diabetes: models, signals, and control

C Cobelli, C Dalla Man, G Sparacino… - IEEE reviews in …, 2009 - ieeexplore.ieee.org
The control of diabetes is an interdisciplinary endeavor, which includes a significant
biomedical engineering component, with traditions of success beginning in the early 1960s …

Blood glucose prediction with variance estimation using recurrent neural networks

J Martinsson, A Schliep, B Eliasson… - Journal of Healthcare …, 2020 - Springer
Many factors affect blood glucose levels in type 1 diabetics, several of which vary largely
both in magnitude and delay of the effect. Modern rapid-acting insulins generally have a …