AIMC Topic: Diabetes Mellitus

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Out-of-distribution reject option method for dataset shift problem in early disease onset prediction.

Scientific reports
Machine learning is increasingly used to predict lifestyle-related disease onset using health and medical data. However, its predictive accuracy for use is often hindered by dataset shift, which refers to discrepancies in data distribution between th...

Enhancing Antidiabetic Drug Selection Using Transformers: Machine-Learning Model Development.

JMIR medical informatics
BACKGROUND: Diabetes affects millions worldwide. Primary care physicians provide a significant portion of care, and they often struggle with selecting appropriate medications.

Global research trends in AI-assisted blood glucose management: a bibliometric study.

Frontiers in endocrinology
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...

Cost-effectiveness of the 3E model in diabetes management: a machine learning approach to assess long-term economic impact.

Frontiers in public health
BACKGROUND: This study investigated the cost-effectiveness and clinical impact of the 3E model (education, empowerment, and economy) in diabetes management using advanced machine learning techniques.

Multitask learning multimodal network for chronic disease prediction.

Scientific reports
Chronic diseases are a critical focus in the management of elderly health. Early disease prediction plays a vital role in achieving disease prevention and reducing the associated burden on individuals and healthcare systems. Traditionally, separate m...

Development and validation of machine learning models for predicting low muscle mass in patients with obesity and diabetes.

Lipids in health and disease
BACKGROUND AND AIMS: Low muscle mass (LMM) is a critical complication in patients with obesity and diabetes, exacerbating metabolic and cardiovascular risks. Novel obesity indices, such as the body roundness index (BRI), conicity index, and relative ...

Managing chronic disease in the digital era: The role of telemedicine apps and platforms.

Przeglad epidemiologiczny
Chronic diseases such as diabetes, heart disease, or asthma are one of the biggest health challenges of the modern world. Their management requires constant care and monitoring, which is a serious burden for healthcare systems. In the digital era, th...

Computational modelling for risk assessment of neurological disorder in diabetes using Hodgkin-Huxley model.

Computer methods and programs in biomedicine
BACKGROUND: Diabetes mellitus, characterized by chronic glucose dysregulation, significantly increases the risk of neurological disorders such as cognitive decline, seizures, and Alzheimer's disease. As neurons depend on glucose for energy, fluctuati...

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals.

Biosensors
Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in diabetes management is maintaining blood glucose levels (BGLs) within a safe range to prevent compli...