AIMC Topic: Glycemic Control
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Global research trends in AI-assisted blood glucose management: a bibliometric study.
Frontiers in endocrinology
May 28, 2025
BACKGROUND: AI-assisted blood glucose management has become a promising method to enhance diabetes care, leveraging technologies like continuous glucose monitoring (CGM) and predictive models. A comprehensive bibliometric analysis is needed to unders...
Predictors of glycaemic improvement in children and young adults with type 1 diabetes and very elevated HbA1c using the MiniMed 780G system.
Diabetes, obesity & metabolism
Jan 20, 2025
AIMS: This study aimed to identify key factors with the greatest influence on glycaemic outcomes in young individuals with type 1 diabetes (T1D) and very elevated glycaemia after 3 months of automated insulin delivery (AID).
Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning.
Cardiovascular diabetology
Nov 26, 2024
BACKGROUND: The burden of atrial fibrillation (AF) in the intensive care unit (ICU) remains heavy. Glycaemic control is important in the AF management. Glycaemic variability (GV), an emerging marker of glycaemic control, is associated with unfavourab...
Predictive Value of Tests
Cause of Death
Atrial Fibrillation
Glycemic Control
Risk Assessment
Blood Glucose
Time Factors
Hospital Mortality
Critical Illness
Intensive Care Units
Female
Reproducibility of Results
Aged, 80 and over
Databases, Factual
Male
Biomarkers
Risk Factors
Middle Aged
Humans
Prognosis
Aged
Machine Learning
Retrospective Studies
Decision Support Techniques
Parental perspectives following the implementation of advanced hybrid closed-loop therapy in children and adolescents with type 1 diabetes and elevated glycaemia.
Diabetic medicine : a journal of the British Diabetic Association
Nov 25, 2024
AIMS: To identify from a parental perspective facilitators and barriers of effective implementation of advanced hybrid closed-loop (AHCL) therapy in children and adolescents with type 1 diabetes (T1D) with elevated glycaemia.
Machine learning assessment of vildagliptin and linagliptin effectiveness in type 2 diabetes: Predictors of glycemic control.
PloS one
Aug 26, 2024
OBJECTIVE: Differential effects of linagliptin and vildagliptin may help us personalize treatment for Type 2 Diabetes Mellitus (T2DM). The current study compares the effect of these drugs on glycated hemoglobin (HbA1c) in an artificial neural network...
Pre-hospital glycemia as a biomarker for in-hospital all-cause mortality in diabetic patients - a pilot study.
Cardiovascular diabetology
May 3, 2024
BACKGROUND: Type 2 Diabetes Mellitus (T2DM) presents a significant healthcare challenge, with considerable economic ramifications. While blood glucose management and long-term metabolic target setting for home care and outpatient treatment follow est...
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...
Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities.
IEEE reviews in biomedical engineering
Jan 12, 2024
OBJECTIVE: Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. Wh...
Predicting poor glycemic control during Ramadan among non-fasting patients with diabetes using artificial intelligence based machine learning models.
Diabetes research and clinical practice
Jul 6, 2022
AIMS: This study aims to predict poor glycemic control during Ramadan among non-fasting patients with diabetes using machine learning models.
Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control.
Annals of epidemiology
Jul 17, 2021
Purpose Machine learning is an attractive tool for identifying heterogeneous treatment effects (HTE) of interventions but generalizability of machine learning derived HTE remains unclear. We examined generalizability of HTE detected using causal fore...
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