AIMC Topic: Blood Glucose Self-Monitoring
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Predictive factors of hypoglycemia in type 2 diabetes: a prospective study using machine learning.
Scientific reports
May 25, 2025
Hypoglycemia is a serious complication in individuals with type 2 diabetes mellitus. Identifying who is most at risk remains challenging due to the non-linear relationships between hypoglycemia and its associated risk factors. The objective of this s...
A deep learning framework for virtual continuous glucose monitoring and glucose prediction based on life-log data.
Scientific reports
May 10, 2025
While continuous glucose monitoring (CGM) has revolutionized metabolic health management, widespread adoption remains limited by cost constraints and usage burden, often resulting in interrupted monitoring periods. We propose a deep learning framewor...
Noninvasive blood glucose monitoring using a dual band microwave sensor with machine learning.
Scientific reports
May 9, 2025
The potential for continuous non-invasive blood glucose monitoring has attracted a lot of interest in the field of medical diagnostics. This paper provides a new shape of a dual-band bandpass filter (DBBPF) acting as a microwave transmission line sen...
The Development and Potential Applications of an Automated Method for Detecting and Classifying Continuous Glucose Monitoring Patterns.
Journal of diabetes science and technology
May 1, 2025
INTRODUCTION: Continuous glucose monitoring (CGM) is emerging as a transformative tool for helping people with diabetes self-manage their glucose and supporting clinicians in effective treatment. Unfortunately, many CGM users, and clinicians, find in...
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
May 1, 2025
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...
Machine Learning-Based Prediction of Large-for-Gestational-Age Infants in Mothers With Gestational Diabetes Mellitus.
The Journal of clinical endocrinology and metabolism
Apr 22, 2025
CONTEXT: Large-for-gestational-age (LGA), one of the most common complications of gestational diabetes mellitus (GDM), has become a global concern. The predictive performance of common continuous glucose monitoring (CGM) metrics for LGA is limited.
[Current applications and challenges of artificial intelligence in diabetes management].
Zhonghua yi xue za zhi
Apr 15, 2025
In recent years, the rapid development of artificial intelligence (AI) has brought innovative opportunities to diabetes management, with significant application potential in various aspects such as prevention, screening, diagnosis, and treatment of d...
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
Jan 1, 2025
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
Jul 1, 2024
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
Jan 1, 2024
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...
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