AIMC Topic: Blood Glucose
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Machine Learning Method and Hyperspectral Imaging for Precise Determination of Glucose and Silicon Levels.
Sensors (Basel, Switzerland)
Feb 18, 2024
This article introduces an algorithm for detecting glucose and silicon levels in solution. The research focuses on addressing the critical need for accurate and efficient glucose monitoring, particularly in the context of diabetic management. Underst...
Improved Glycemic Control through Robot-Assisted Remote Interview for Outpatients with Type 2 Diabetes: A Pilot Study.
Medicina (Kaunas, Lithuania)
Feb 15, 2024
: Our research group developed a robot-assisted diabetes self-management monitoring system to support Certified Diabetes Care and Education Specialists (CDCESs) in tracking the health status of patients with type 2 diabetes (T2D). This study aimed to...
DiaNet v2 deep learning based method for diabetes diagnosis using retinal images.
Scientific reports
Jan 18, 2024
Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and acc...
Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models.
Frontiers in public health
Jan 15, 2024
INTRODUCTION: Decades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a p...
A Smart Sensing Technologies-Based Intelligent Healthcare System for Diabetes Patients.
Sensors (Basel, Switzerland)
Dec 1, 2023
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...
Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses.
Journal of biomedical optics
Aug 1, 2023
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
Jul 20, 2023
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
Apr 11, 2023
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
Jan 20, 2023
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
Jan 11, 2023
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...
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