Diabetes

A Prospective Pilot Study Demonstrating Noninvasive Calibration-Free Glucose Measurement.

BACKGROUND: Glucose is an essential molecule in energy metabolism. Dysregulated glucose metabolism, the defining feature of diabetes, requires active monitoring and treatment to prevent significant morbidity and mortality. Current technologies for intermittent and continuous glucose measurement are invasive. Noninvasive glucose measurement would eliminate this barrier toward making glucose monitor...

The triglyceride-glucose index and its obesity-related derivatives as predictors of all-cause and cardiovascular mortality in hypertensive patients: insights from NHANES data with machine learning analysis.

BACKGROUND: Hypertension (HTN) is a global public health concern and a major risk factor for cardiovascular disease (CVD) and mortality. Insulin resistance (IR) plays a crucial role in HTN-related metabolic dysfunction, but its assessment remains challenging. The triglyceride-glucose (TyG) index and its derivatives (TyG-BMI, TyG-WC, and TyG-WHtR) have emerged as reliable IR markers. In this study,...

Optimizing warfarin dosing in diabetic patients through BERT model and machine learning techniques.

This study highlights the importance of evaluating warfarin dosing in diabetic patients, who require careful anticoagulation management. With rising r...

Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models.

Diabetic retinopathy, a retinal disorder resulting from diabetes mellitus, is a prominent cause of visual degradation and loss among the global popula...

A safe-enhanced fully closed-loop artificial pancreas controller based on deep reinforcement learning.

Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden...

Acute effect of endurance exercise on human milk insulin concentrations: a randomised cross-over study.

INTRODUCTION: Insulin is present in human milk and its concentration correlates with maternal circulating levels. Studies on the association between h...

A robust and generalized framework in diabetes classification across heterogeneous environments.

Diabetes mellitus (DM) represents a major global health challenge, affecting a diverse range of demographic populations across all age groups. It has ...

Efficient diagnosis of diabetes mellitus using an improved ensemble method.

Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been w...

Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.

Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of...

Machine-learning model based on ultrasomics for non-invasive evaluation of fibrosis in IgA nephropathy.

OBJECTIVES: To develop and validate an ultrasomics-based machine-learning (ML) model for non-invasive assessment of interstitial fibrosis and tubular ...

Artificial intelligence-enabled obesity prediction: A systematic review of cohort data analysis.

BACKGROUND: Obesity, now the fifth leading global cause of death, has seen a dramatic rise in prevalence over the last forty years. It significantly i...

Optimising deep learning models for ophthalmological disorder classification.

Fundus imaging, a technique for recording retinal structural components and anomalies, is essential for observing and identifying ophthalmological dis...

Enhanced Phenotype Identification of Common Ocular Diseases in Real-World Datasets.

OBJECTIVE: For studies using real-world data, accurately identifying patients with phenotypes of interest is challenging. To identify cohorts of inter...

Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction.

Diabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicti...

Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.

INTRODUCTION: Type 2 diabetes (T2D) is a complex metabolic disorder with significant global health implications. Understanding the molecular mechanism...

Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study.

BACKGROUND: Type 2 diabetes (T2D) is a leading cause of premature morbidity and mortality globally and affects more than 100 million people in the wor...

Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence.

BACKGROUND: Diabetic nephropathy (DN) is a complication of systemic microvascular disease in diabetes mellitus. Abnormal glycolysis has emerged as a p...

Deep learning based prediction of depression and anxiety in patients with type 2 diabetes mellitus using regional electronic health records.

BACKGROUND: Depression and anxiety are prevalent mental health conditions among individuals with type 2 diabetes mellitus (T2DM), who exhibit unique v...

Machine learning-driven Raman spectroscopy: A novel approach to lipid profiling in diabetic kidney disease.

Diabetes mellitus is a chronic metabolic disease that increasingly affects people every year. It is known that with its progression and poor managemen...

Deep learning generalization for diabetic retinopathy staging from fundus images.

. Diabetic retinopathy (DR) is a serious diabetes complication that can lead to vision loss, making timely identification crucial. Existing data-drive...