Blood glucose prediction for type 1 diabetes using generative adversarial networks
Maintaining blood glucose in a target range is essential for people living with Type 1
diabetes in order to avoid excessive periods in hypoglycemia and hyperglycemia which can …
diabetes in order to avoid excessive periods in hypoglycemia and hyperglycemia which can …
Incorporating glucose variability into glucose forecasting accuracy assessment using the new glucose variability impact index and the prediction consistency index: An …
C Mosquera-Lopez, PG Jacobs - Journal of Diabetes …, 2022 - journals.sagepub.com
Background: In this work, we developed glucose forecasting algorithms trained and
evaluated on a large dataset of free-living people with type 1 diabetes (T1D) using closed …
evaluated on a large dataset of free-living people with type 1 diabetes (T1D) using closed …
Video based cocktail causal container for blood pressure classification and blood glucose prediction
C Zhang, E Jovanov, H Liao, YT Zhang… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
With the development of modern cameras, more physiological signals can be obtained from
portable devices like smartphone. Some hemodynamically based non-invasive video …
portable devices like smartphone. Some hemodynamically based non-invasive video …
[HTML][HTML] Causality analysis in type 1 diabetes mellitus with application to blood glucose level prediction
Effective control of blood glucose level (BGL) is the key factor in the management of type 1
diabetes mellitus (T1D). BGL prediction is an important tool to help maximise the time BGL is …
diabetes mellitus (T1D). BGL prediction is an important tool to help maximise the time BGL is …
[HTML][HTML] Intelligent control with artificial neural networks for automated insulin delivery systems
JLCB de Farias, WM Bessa - Bioengineering, 2022 - mdpi.com
Type 1 diabetes mellitus is a disease that affects millions of people around the world. Recent
progress in embedded devices has allowed the development of artificial pancreas that can …
progress in embedded devices has allowed the development of artificial pancreas that can …
Impartial feature selection using multi-agent reinforcement learning for adverse glycemic event prediction
We developed an attention model to predict future adverse glycemic events 30 min in
advance based on the observation of past glycemic values over a 35 min period. The …
advance based on the observation of past glycemic values over a 35 min period. The …
Accuracy versus reliability-based modelling approaches for medical decision making
Forecasting in the medical domain is critical to the quality of decisions made by physicians,
patients, and health planners. Modeling is one of the most important components of decision …
patients, and health planners. Modeling is one of the most important components of decision …
[HTML][HTML] Reducing high-risk glucose forecasting errors by evolving interpretable models for type 1 diabetes
Diabetes mellitus is a metabolic disease involving high blood glucose levels that can lead to
serious medical consequences. Hence, for diabetic patients the prediction of future glucose …
serious medical consequences. Hence, for diabetic patients the prediction of future glucose …
Intelligent ensemble deep learning system for blood glucose prediction using genetic algorithms
Forecasting blood glucose (BG) values for patients can help prevent hypoglycemia and
hyperglycemia events in advance. To this end, this study proposes an intelligent ensemble …
hyperglycemia events in advance. To this end, this study proposes an intelligent ensemble …
[HTML][HTML] DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions
Objective digital data is scarce yet needed in many domains to enable research that can
transform the standard of healthcare. While data from consumer-grade wearables and …
transform the standard of healthcare. While data from consumer-grade wearables and …