AIMC Topic: Diabetes Mellitus, Type 2
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Type 2 Diabetes in Taiwan: Unmasking Influential Factors Through Advanced Predictive Modeling.
Journal of diabetes research
May 27, 2025
Type 2 diabetes (T2D) is influenced by lifestyle, genetics, and environmental conditions. By utilizing machine learning techniques, we can enhance the precision of T2D risk prediction by analyzing the complex interactions among these variables. This...
Continuous glucose monitoring combined with artificial intelligence: redefining the pathway for prediabetes management.
Frontiers in endocrinology
May 26, 2025
Prediabetes represents an early stage of glucose metabolism disorder with significant public health implications. Although traditional lifestyle interventions have demonstrated some efficacy in preventing the progression to type 2 diabetes, their lim...
Resting-State Functional MRI Reveals Altered Seed-Based Connectivity in Diabetic Osteoporosis Patients.
Clinical interventions in aging
May 21, 2025
BACKGROUND: Diabetic osteoporosis (DOP) can cause abnormal brain neural activity, but its mechanism is still unclear. This study aims to further explore the abnormal functional connectivity between different brain regions based on the team's previous...
Exploring potential diagnostic markers and therapeutic targets for type 2 diabetes mellitus with major depressive disorder through bioinformatics and in vivo experiments.
Scientific reports
May 15, 2025
Type 2 diabetes mellitus (T2DM) and Major depressive disorder (MDD) act as risk factors for each other, and the comorbidity of both significantly increases the all-cause mortality rate. Therefore, studying the diagnosis and treatment of diabetes with...
Machine Learning Models for Predicting Type 2 Diabetes Complications in Malaysia.
Asia-Pacific journal of public health
Apr 18, 2025
This study aimed to develop machine learning (ML) models to predict diabetic complications in patients with type 2 diabetes (T2D) in Malaysia. Data from the Malaysian National Diabetes Registry and Death Register were used to develop predictive model...
The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care Physicians.
Journal of primary care & community health
Apr 16, 2025
BACKGROUND: Artificial intelligence (AI) platforms can potentially enhance clinical decision-making (CDM) in primary care settings. OpenEvidence (OE), an AI tool, draws from trusted sources to generate evidence-based medicine (EBM) recommendations to...
Gut microbiome research: Revealing the pathological mechanisms and treatment strategies of type 2 diabetes.
Diabetes, obesity & metabolism
Apr 15, 2025
The high prevalence and disability rate of type 2 diabetes (T2D) caused a huge social burden to the world. Currently, new mechanisms and therapeutic approaches that may affect this disease are being sought. With in-depth research on the pathogenesis ...
Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus.
Renal failure
Apr 7, 2025
BACKGROUND: Diabetic kidney disease (DKD) is a common and serious complication of diabetic mellitus (DM). More sensitive methods for early DKD prediction are urgently needed. This study aimed to set up DKD risk prediction models based on machine lear...
Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning.
Scientific reports
Apr 5, 2025
The burden of diabetic foot ulcers (DFU) is exacerbated in diabetic patients with concomitant arteriosclerotic occlusion disease (ASO) in the lower extremities, who experience more severe symptoms and poorer prognoses. The study aims to develop a pre...
Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.
Frontiers in endocrinology
Mar 27, 2025
PURPOSE: We aimed to identify the mitochondria-related feature genes associated with type 2 diabetes mellitus and explore their potential roles in immune cell infiltration.
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