Diabetes

Identification of macrophage-associated diagnostic biomarkers and molecular subtypes in gestational diabetes mellitus based on machine learning.

Gestational diabetes mellitus (GDM) is a common metabolic disorder during pregnancy, involving multiple immune and inflammatory factors. Macrophages play a crucial role in its development. This study integrated scRNA-seq and RNA-seq data to explore macrophage-related diagnostic genes and GDM subtypes. For scRNA-seq data, cell clusters were annotated using the SingleR package and validated with mar...

Association of urinary metal elements with sarcopenia and glucose metabolism abnormalities: Insights from NHANES data using machine learning approaches.

BACKGROUND: Sarcopenia, a condition marked by the decline of skeletal muscle mass and function, is prevalent in the elderly and closely linked to abnormal glucose metabolism, particularly type 2 diabetes. Hyperglycemia can increase the formation of advanced glycation end-products (AGEs) in muscle proteins, impairing muscle function. Additionally, deficiencies in trace minerals are associated with ...

Case-control study combined with machine learning techniques to identify key genetic variations in GSK3B that affect susceptibility to diabetic kidney diseases.

The role of genetic susceptibility in early warning and precise treatment of diabetic kidney disease (DKD) requires further investigation. A case-cont...

Prediction and accuracy improvement of insulin pump in-fusion deviation based on LSTM and PID.

In order to further improve the injection precision of the PH300 insulin pump, this paper optimizes and improves the mechanical structure and control ...

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

Machine learning is increasingly used to predict lifestyle-related disease onset using health and medical data. However, its predictive accuracy for u...

Data-driven diabetes mellitus prediction and management: a comparative evaluation of decision tree classifier and artificial neural network models along with statistical analysis.

Diabetes Mellitus is a chronic metabolic disorder affecting a substantial global population leading to complications such as retinopathy, nephropathy,...

The Application of Machine Learning in Warfarin Dose Precision for Diabetic Patients Treated with Statins: A Comparative Study.

PURPOSE: To evaluate the impact of statin therapy on warfarin dose requirements in diabetic patients and to assess the performance of various machine ...

Assessing the stress hyperglycemia ratio in hyperlipidemia patients to predict all-cause mortality: A retrospective cohort study.

BACKGROUND: The Stress Hyperglycemia Ratio (SHR) reflects stress-related hyperglycemia and is linked to poor outcomes in various diseases. This study ...

Revolutionizing diabetes care: the role of artificial intelligence in prevention, diagnosis, and patient care.

UNLABELLED: Millions of people worldwide have diabetes, a disease that is becoming more common and has substantial socioeconomic costs. Artificial int...

Application of machine learning algorithms to identify risk factors for depression in type 2 diabetes mellitus patients: A Taiwan diabetes registry study.

BACKGROUND: We analyzed variables reported during routine clinical practice using a registrational database to estimate risk factors for depression in...

Evaluating the impact of metabolic indicators and scores on cardiovascular events using machine learning.

Cardiovascular diseases such as coronary artery disease, myocardial infarction, and heart failure impact millions of people annually globally and are ...

Artificial Intelligence improves follow-up appointment uptake for diabetic retinal assessment: a systematic review and meta-analysis.

BACKGROUND/OBJECTIVES: Artificial intelligence (AI) assessment of diabetic retinopathy (DR) instead of scarce trained specialists could potentially in...

Integration of proteomics and artificial intelligence-driven OCT biomarker analysis in central retinal vein occlusion.

Retinal OCT biomarker analysis by artificial intelligence (AI) has not previously been integrated with proteomics. Here, we combined the two technique...

DPP-IV inhibitory peptides from highland barley via machine learning and multi-scale validation.

Highland barley has shown potential in regulating blood glucose and may serve as a natural source of dipeptidyl peptidase-IV (DPP-IV) inhibitors. In t...

Artificial Intelligence's Role in Improving Adverse Pregnancy Outcomes: A Scoping Review and Consideration of Ethical Issues.

: Adverse pregnancy outcomes (APOs), which include hypertensive disorders of pregnancy (gestational hypertension, preeclampsia, and related disorders)...

A machine learning model for predicting anatomical response to Anti-VEGF therapy in diabetic macular edema.

PURPOSE: To develop a machine learning model to predict anatomical response to anti-VEGF therapy in patients with diabetic macular edema (DME).

Automatic detection of optic canal fractures and recognition and segmentation of anatomical structures in the orbital apex based on artificial intelligence.

BACKGROUND AND OBJECTIVES: Traumatic optic neuropathy (TON) caused by optic canal fractures (OCF) can result in severe visual impairment, even blindne...

A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008-2012, 2017-2021).

BACKGROUND: Machine learning technology that uses available clinical data to predict diabetic retinopathy (DR) can be highly valuable in medical setti...

Estimating the Risk of Lower Extremity Complications in Adults Newly Diagnosed With Diabetic Polyneuropathy: Retrospective Cohort Study.

BACKGROUND: Diabetes-related lower extremity complications, such as foot ulceration and amputation, are on the rise, currently affecting nearly 131 mi...

Beyond the Trend Arrow: Potential Value of Artificial Intelligence-Supported Glucose Predictions for People with Type 1 Diabetes Using Continuous Glucose Monitoring Systems.

Advances in diabetes technologies such as continuous glucose monitoring (CGM) have provided significant opportunities to improve glycemic and quality-...