AIMC Topic: Diabetes Mellitus, Type 2
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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...
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.
Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS.
BMC public health
Mar 26, 2025
OBJECTIVE: The incidence of Type 2 Diabetes Mellitus (T2DM) continues to rise steadily, significantly impacting human health. Early prediction of pre-diabetic risks has emerged as a crucial public health concern in recent years. Machine learning meth...
Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007-2018.
BMJ open
Mar 22, 2025
OBJECTIVES: This study aimed to compare the performance of five machine learning algorithms to predict diabetes mellitus based on lifestyle factors (diet and physical activity).
Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia.
Frontiers in endocrinology
Mar 20, 2025
BACKGROUND: Medication adherence plays a crucial role in determining the health outcomes of patients, particularly those with chronic conditions like type 2 diabetes. Despite its significance, there is limited evidence regarding the use of machine le...
Deciphering the Pharmacological Potential of Kouqiangjie Formula for the Treatment of Diabetic Periodontitis Based on Network Pharmacology, Machine Learning, Molecular Dynamics, and Animal Experiments.
Drug design, development and therapy
Mar 20, 2025
BACKGROUND: Periodontitis (PD) and type 2 diabetes mellitus (T2DM) represent interlinked global health burdens, commonly causing significant clinical complications when coincident. Therefore, managing both conditions (T2DM with periodontitis, DP) sim...
Predicting the risks of stroke, cardiovascular disease, and peripheral vascular disease among people with type 2 diabetes with artificial intelligence models: A systematic review and meta-analysis.
Narra J
Mar 19, 2025
Macrovascular complications, including stroke, cardiovascular disease (CVD), and peripheral vascular disease (PVD), significantly contribute to morbidity and mortality in individuals with type 2 diabetes mellitus (T2DM). The aim of this study was to ...
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