AI Medical Compendium Topic

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Glycemic Control

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Real-Time AI-Assisted Insulin Titration System for Glucose Control in Patients With Type 2 Diabetes: A Randomized Clinical Trial.

JAMA network open
IMPORTANCE: Type 2 diabetes (T2D) is one of the most prevalent chronic diseases in the world. Insulin titration for glycemic control in T2D is crucial but limited by the lack of personalized and real-time tools.

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
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).

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
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.

Predicting poor glycemic control during Ramadan among non-fasting patients with diabetes using artificial intelligence based machine learning models.

Diabetes research and clinical practice
AIMS: This study aims to predict poor glycemic control during Ramadan among non-fasting patients with diabetes using machine learning models.

Machine learning assessment of vildagliptin and linagliptin effectiveness in type 2 diabetes: Predictors of glycemic control.

PloS one
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...

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities.

IEEE reviews in biomedical engineering
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...

Improved Glycemic Control through Robot-Assisted Remote Interview for Outpatients with Type 2 Diabetes: A Pilot Study.

Medicina (Kaunas, Lithuania)
: 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...

Pre-hospital glycemia as a biomarker for in-hospital all-cause mortality in diabetic patients - a pilot study.

Cardiovascular diabetology
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...

Prediction of Poor Glycemic Control in Children with Type 1 Diabetes.

Studies in health technology and informatics
This study developed and validated a machine learning model for predicting glycemic control in children with type 1 diabetes at the time of diagnosis, revealing age at diagnosis as the most informative predictor.