AIMC Topic: Glycated Hemoglobin

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Efficacy of FiberMore, an AI-Based mHealth Intervention to Increase Dietary Fiber Intake Among Type 2 Diabetes Patients: Protocol for a Pilot Randomized Controlled Trial.

JMIR research protocols
BACKGROUND: A high intake of dietary fiber has been shown to improve glycemic control and decrease hyperinsulinemia in people living with type 2 diabetes (T2D). T2D patients in Japan consume less than the recommended amount of fiber. Based on finding...

Opportunistic screening of type 2 diabetes with deep metric learning using electronic health records.

Scientific reports
Deep learning models leveraging electronic health records (EHR) for opportunistic screening of type 2 diabetes (T2D) can improve current practices by identifying individuals who may need further glycemic testing. Accurate onset prediction and subtypi...

Aetiological clustering of newly diagnosed type 2 diabetes using machine learning: a retrospective cross-sectional study in Dubai, UAE.

BMJ open
OBJECTIVES: Type 2 diabetes (T2D) is a complex disease with a heterogeneous clinical presentation. Recently, five distinct clusters of T2D have been identified in the Emirati population of long-standing T2D with complications. This study aimed to val...

My diabetes care: an AI-based mobile app with conversational agent for type 2 diabetes self-management.

Scientific reports
Despite advancements in modern healthcare, diabetes mellitus remains a lifelong, incurable condition. Empowering patients through health education and self-management is essential in preventing disease progression. This study evaluates the effectiven...

Development of a machine learning-based interface for insulin dependency prediction using clinical data.

Scientific reports
Diabetes mellitus is a major global health burden, and early identification of insulin dependency is important for timely intervention. This study developed an artificial intelligence-based diagnostic system using a real-world clinical dataset of 100...

Dynamic HGI trajectories and their impact on survival in patients with sepsis: a machine learning prognostic model.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]
BACKGROUND: Previous studies have indicated a correlation between the glycosylated hemoglobin index (HGI) and the prognosis of patients with sepsis. However, the impact of its dynamic fluctuations on patient outcomes remains insufficiently explored. ...

Association between hemoglobin glycation index and 28-day all-cause mortality in acute myocardial infarction patients: Analysis of the MIMIC-IV database.

PloS one
Acute myocardial infarction (AMI) substantially fuels the worldwide escalation in both morbidity and mortality. The hemoglobin glycation index (HGI) is linked to a range of undesirable outcomes, but its relationship with short-term outcomes in AMI pa...

Age-related variation in hemoglobin glycation index and stroke mortality: mediation and machine learning in a cohort study.

Scientific reports
To investigate the associations between both age and the hemoglobin glycation index (HGI) and the 30-day and 1-year mortality in ischemic stroke (IS) patients and to analyze the mediating effect of the HGI on the relationship between age and mortalit...