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Blood Glucose

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A Smart Sensing Technologies-Based Intelligent Healthcare System for Diabetes Patients.

Sensors (Basel, Switzerland)
An Artificial Intelligence (AI)-enabled human-centered smart healthcare monitoring system can be useful in life saving, specifically for diabetes patients. Diabetes and heart patients need real-time and remote monitoring and recommendation-based medi...

Prediction of Hypoglycemia From Continuous Glucose Monitoring in Insulin-Treated Patients With Type 2 Diabetes Using Transfer Learning on Type 1 Diabetes Data: A Deep Transfer Learning Approach.

Journal of diabetes science and technology
BACKGROUND: Hypoglycemia is common in insulin-treated type 2 diabetes (T2D) patients, which can lead to decreased quality of life or premature death. Deep learning models offer promise of accurate predictions, but data scarcity poses a challenge. Thi...

Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses.

Journal of biomedical optics
SIGNIFICANCE: Diabetes is a prevalent disease worldwide that can cause severe health problems. Accurate blood glucose detection is crucial for diabetes management, and noninvasive methods can be more convenient and less painful than traditional finge...

High-precision prediction of blood glucose concentration utilizing Fourier transform Raman spectroscopy and an ensemble machine learning algorithm.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Raman spectroscopy has gained popularity in analyzing blood glucose levels due to its non-invasive identification and minimal interference from water. However, the challenge lies in how to accurately predict blood glucose concentrations in human bloo...

A deep learning nomogram of continuous glucose monitoring data for the risk prediction of diabetic retinopathy in type 2 diabetes.

Physical and engineering sciences in medicine
Continuous glucose monitoring (CGM) data analysis will provide a new perspective to analyze factors related to diabetic retinopathy (DR). However, the problem of visualizing CGM data and automatically predicting the incidence of DR from CGM is still ...

Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes.

Frontiers in endocrinology
OBJECTIVE: For the patients who are suffering from type 2 diabetes, blood glucose level could be affected by multiple factors. An accurate estimation of the trajectory of blood glucose is crucial in clinical decision making. Frequent glucose measurem...

Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study.

Journal of diabetes science and technology
BACKGROUND: The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitor...

Personalized Blood Glucose Prediction for Type 1 Diabetes Using Evidential Deep Learning and Meta-Learning.

IEEE transactions on bio-medical engineering
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 new paradigm of algorithm design for personalized blood glucose (BG) predicti...

Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? A Systematic Review of Basal Insulin Dose Guidance.

Journal of diabetes science and technology
BACKGROUND: Real-world studies of people with type 2 diabetes (T2D) have shown insufficient dose adjustment during basal insulin titration in clinical practice leading to suboptimal treatment. Thus, 60% of people with T2D treated with insulin do not ...

Quantitative measurement of blood glucose influenced by multiple factors via photoacoustic technique combined with optimized wavelet neural networks.

Journal of biophotonics
In this work, the photoacoustic (PA) quantitative measurement of blood glucose concentration (BGC) influenced by multiple factors was firstly investigated. A set of PA detection system of blood glucose considering the comprehensive influence of five ...