AIMC Topic: Blood Glucose Self-Monitoring

Clear Filters Showing 81 to 90 of 95 articles

Continuous glucose monitoring using machine learning models and IoT device data: A meta-analysis.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Machine learning offers diverse options for effectively managing blood glucose levels in diabetes patients. Selecting the right ML algorithm is critical given the array of available choices. Integrating data from IoT devices presents prom...

Enhancing Wearable based Real-Time Glucose Monitoring via Phasic Image Representation Learning based Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In the U.S., over a third of adults are pre-diabetic, with 80% unaware of their status. This underlines the need for better glucose monitoring to prevent type 2 diabetes and related heart diseases. Existing wearable glucose monitors are limited by th...

Binary fire hawks optimizer with deep learning driven non-invasive diabetes detection and classification.

Bratislavske lekarske listy
Non-invasive diabetes detection refers to the utilization and development of technologies and methods that can monitor and diagnose diabetes without requiring invasive procedures, namely invasive glucose monitoring or blood sampling. The objective is...

Artificial Intelligence in Efficient Diabetes Care.

Current diabetes reviews
Diabetes is a chronic disease that is not easily curable but can be managed efficiently. Artificial Intelligence is a powerful tool that may help in diabetes prediction, continuous glucose monitoring, Insulin injection guidance, and other areas of di...

Glucose trajectory prediction by deep learning for personal home care of type 2 diabetes mellitus: modelling and applying.

Mathematical biosciences and engineering : MBE
Glucose management for people with type 2 diabetes mellitus is essential but challenging due to the multi-factored and chronic disease nature of diabetes. To control glucose levels in a safe range and lessen abnormal glucose variability efficiently a...

Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype.

Journal of the American Medical Informatics Association : JAMIA
We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to i...

Knowledge-driven dictionaries for sparse representation of continuous glucose monitoring signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Continuous glucose monitoring (CGM) of patients with diabetes allows the effective management of the disease and reduces the risk of hypoglycemic or hyperglycemic episodes. Towards this goal, the development of reliable CGM models is essential for re...

Decision Support in Diabetes Care: The Challenge of Supporting Patients in Their Daily Living Using a Mobile Glucose Predictor.

Journal of diabetes science and technology
BACKGROUND: In type 1 diabetes mellitus (T1DM), patients play an active role in their own care and need to have the knowledge to adapt decisions to their daily living conditions. Artificial intelligence applications can help people with type 1 diabet...