Diabetes

Personalized glucose forecasting for people with type 1 diabetes using large language models.

BACKGROUND AND OBJECTIVE: Type 1 Diabetes (T1D) is an autoimmune disease that requires exogenous insulin via Multiple Daily Injections (MDIs) or subcutaneous pumps to maintain targeted glucose levels. Despite the advances in Continuous Glucose Monitoring (CGM), controlling glucose levels remains challenging. Large Language Models (LLMs) have produced impressive results in text processing, but thei...

Artificial intelligence-based personalised rituximab treatment protocol in membranous nephropathy (iRITUX): protocol for a multicentre randomised control trial.

INTRODUCTION: Membranous nephropathy is an autoimmune kidney disease and the most common cause of nephrotic syndrome in non-diabetic Caucasian adults. Rituximab is now recommended as first-line therapy for membranous nephropathy. However, Kidney Disease Improving Global Outcomes guidelines do not recommend any specific protocol. Rituximab bioavailability is reduced in patients with membranous neph...

AI-Enabled Screening for Retinopathy of Prematurity in Low-Resource Settings.

IMPORTANCE: Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness worldwide. If detected and treated early, ROP-ass...

Machine Learning Identification of Neutrophil Extracellular Trap-Related Genes as Potential Biomarkers and Therapeutic Targets for Bronchopulmonary Dysplasia.

Neutrophil extracellular traps (NETs) play a key role in the development of bronchopulmonary dysplasia (BPD), yet their molecular mechanisms in contri...

VisionGuard: enhancing diabetic retinopathy detection with hybrid deep learning.

OBJECTIVES: Early detection of diabetic retinopathy (DR) and timely intervention are critical for preventing vision loss. Recently, deep learning tech...

Machine learning for risk prediction of acute kidney injury in patients with diabetes mellitus combined with heart failure during hospitalization.

This study aimed to develop a machine learning (ML) model for predicting the risk of acute kidney injury (AKI) in diabetic patients with heart failure...

Real-World Type 2 Diabetes Second-Line Treatment Allocation Among Patients.

OBJECTIVE: This study aimed to evaluate the impact of socioeconomic disparities on the allocation of second-line treatments among patients with type 2...

Hybrid deep learning framework for diabetic retinopathy classification with optimized attention AlexNet.

Diabetic retinopathy (DR) is a chronic condition associated with diabetes that can lead to vision impairment and, if not addressed, may progress to ir...

Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.

PURPOSE: We aimed to identify the mitochondria-related feature genes associated with type 2 diabetes mellitus and explore their potential roles in imm...

Future horizons in diabetes: integrating AI and personalized care.

Diabetes is a global health crisis with rising incidence, mortality, and economic burden. Traditional markers like HbA1c are insufficient for capturin...

Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS.

OBJECTIVE: The incidence of Type 2 Diabetes Mellitus (T2DM) continues to rise steadily, significantly impacting human health. Early prediction of pre-...

Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis.

: Patent ductus arteriosus (PDA) is common in newborns, being associated with high morbidity and mortality. While maternal and neonatal conditions are...

An interpreting machine learning models to predict amputation risk in patients with diabetic foot ulcers: a multi-center study.

BACKGROUND: Diabetic foot ulcers (DFUs) constitute a significant complication among individuals with diabetes and serve as a primary cause of nontraum...

Which approach better predicts diabetes: Traditional econometric methods or machine learning? Evidence from a cross-sectional study in South Korea.

To prevent chronic disease from getting worse, it is important to detect and predict it at an early stage. Therefore, the accuracy of the prediction i...

Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007-2018.

OBJECTIVES: This study aimed to compare the performance of five machine learning algorithms to predict diabetes mellitus based on lifestyle factors (d...

Machine learning-based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes, marked by symptoms like hyperalgesia, numbness, and swe...

Personalized Blood Glucose Forecasting From Limited CGM Data Using Incrementally Retrained LSTM.

For people with Type 1 diabetes (T1D), accurate blood glucose (BG) forecasting is crucial for the effective delivery of insulin by Artificial Pancreas...

The relationship between epigenetic biomarkers and the risk of diabetes and cancer: a machine learning modeling approach.

INTRODUCTION: Epigenetic biomarkers are molecular indicators of epigenetic changes, and some studies have suggested that these biomarkers have predict...

Finding Optimal Alphabet for Encoding Daily Continuous Glucose Monitoring Time Series Into Compressed Text.

BACKGROUND: The emergence of continuous glucose monitoring (CGM) devices has not only revolutionized diabetes management but has also opened new avenu...

Machine Learning-Assisted Portable Dual-Readout Biosensor for Visual Detection of Milk Allergen.

Beta-lactoglobulin (β-LG), the primary allergen in cow's milk, makes developing a rapid, sensitive, and convenient detection method essential for indi...