AIMC Topic: Blood Glucose

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A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease.

Scientific reports
Glycogen storage disease (GSD) is a group of rare inherited metabolic disorders characterized by abnormal glycogen storage and breakdown. These disorders are caused by mutations in G6PC1, which is essential for proper glucose storage and metabolism. ...

Quantitative detection of serum biochemical indexes via UV-Vis-NIRS combined with deep neural networks.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
To achieve rapid, cost-efficient, convenient and accurate detection of five clinical serum biochemical indexes, namely glucose (GLU), triglycerides (TG), total cholesterol (TC), total protein (TP) and albumin (ALB), ultraviolet-visible-near infrared ...

Personalized glucose forecasting for people with type 1 diabetes using large language models.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Type 1 Diabetes (T1D) is an autoimmune disease that requires exogenous insulin via Multiple Daily Injections (MDIs) or subcutaneous pumps to maintain targeted glucose levels. Despite the advances in Continuous Glucose Monito...

Effectiveness of a community intervention program on healthy lifestyles (PREDICOL) among adults with prediabetes in two Latin American cities: A quasi-experimental study.

Primary care diabetes
PURPOSE: This study aimed to measure the impact of a community-based lifestyle modification intervention program on the Health-Related Quality of Life (HRQoL) of adults with prediabetes in two Latin American cities.

Which approach better predicts diabetes: Traditional econometric methods or machine learning? Evidence from a cross-sectional study in South Korea.

Computers in biology and medicine
To prevent chronic disease from getting worse, it is important to detect and predict it at an early stage. Therefore, the accuracy of the prediction is particularly important. To investigate the accuracy of different methods, this study compares the ...

Personalized Blood Glucose Forecasting From Limited CGM Data Using Incrementally Retrained LSTM.

IEEE transactions on bio-medical engineering
For people with Type 1 diabetes (T1D), accurate blood glucose (BG) forecasting is crucial for the effective delivery of insulin by Artificial Pancreas (AP) systems. Deep learning frameworks like Long Short-Term-Memory (LSTM) have been widely used to ...

Surrogate markers of insulin resistance and coronary artery disease in type 2 diabetes: U-shaped TyG association and insights from machine learning integration.

Lipids in health and disease
BACKGROUND: Surrogate insulin resistance (IR) indices are simpler and more practical alternatives to insulin-based IR indicators for clinical use. This study explored the association between surrogate IR indices, including triglyceride-glucose index ...

Machine Learning-Driven D-Glucose Prediction Using a Novel Biosensor for Non-Invasive Diabetes Management.

Biosensors
Developing reliable noninvasive diagnostic and monitoring systems for diabetes remains a significant challenge, especially in the e-healthcare domain, due to computational inefficiencies and limited predictive accuracy in current approaches. The curr...