Diabetes

Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography.

BACKGROUND: To apply machine learning (ML) algorithms to perform multiclass diabetic retinopathy (DR...

Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence-Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study.

BACKGROUND: The clinical management of type 2 diabetes mellitus (T2DM) presents a significant challe...

Dynamic Individualized Risk Prediction in IgA Nephropathy: Entering the Era of Artificial Intelligence.

Use of an Artificial Intelligence-Generated Vascular Severity Score Improved Plus Disease Diagnosis in Retinopathy of Prematurity.

PURPOSE: To evaluate whether providing clinicians with an artificial intelligence (AI)-based vascula...

Artificial Intelligence Applications in Diabetic Retinopathy: What We Have Now and What to Expect in the Future.

Diabetic retinopathy (DR) is a major complication of diabetes mellitus and is a leading cause of vis...

Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients.

OBJECTIVE: The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major pub...

Identification of novel biomarkers to distinguish clear cell and non-clear cell renal cell carcinoma using bioinformatics and machine learning.

Renal cell carcinoma (RCC), accounting for 90% of all kidney cancer, is categorized into clear cell ...

Apoptosis and NETotic cell death affect diabetic nephropathy independently: An study integrative study encompassing bioinformatics, machine learning, and experimental validation.

OBJECTIVE: Although programmed cell death (PCD) and diabetic nephropathy (DN) are intrinsically conn...

Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach.

Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Alth...

Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals.

INTRODUCTION: None of the studies of type 2 diabetes (T2D) subtyping to date have used linked popula...

Prediction of pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II based on clinical risk.

OBJECTIVE: To assess the effectiveness of different machine learning models in estimating the pharma...

Au nanozyme-based colorimetric sensor array integrates machine learning to identify and discriminate monosaccharides.

As different monosaccharides exhibit different redox characteristics, this paper presented a novel c...

Integrated biomarker profiling for predicting the response of type 2 diabetes to metformin.

AIM: To explore biomarkers that can predict the response of type 2 diabetes (T2D) patients to metfor...

Utilizing machine learning algorithms for precise discrimination of glycosuria in fluorescence spectroscopic data.

Fluorescence spectroscopy coupled with a random forest machine learning algorithm offers a promising...

Explainable hypoglycemia prediction models through dynamic structured grammatical evolution.

Effective blood glucose management is crucial for people with diabetes to avoid acute complications....

A Scalable Application of Artificial Intelligence-Driven Insulin Titration Program to Transform Type 2 Diabetes Management.

Despite new pharmacotherapy, most patients with long-term type 2 diabetes are still hyperglycemic. ...

Machine learning-based diagnostic prediction of IgA nephropathy: model development and validation study.

IgA nephropathy progresses to kidney failure, making early detection important. However, definitive ...

Development and Validation of a Machine Learning Model to Predict Weekly Risk of Hypoglycemia in Patients with Type 1 Diabetes Based on Continuous Glucose Monitoring.

The aim of this study was to develop and validate a prediction model based on continuous glucose mo...

An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health.

BACKGROUND: Diabetes is a chronic condition that can result in many long-term physiological, metabol...

Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013-2023).

BACKGROUND AND OBJECTIVES: Optical coherence tomography (OCT) has ushered in a transformative era in...

Machine learning for prediction of chronic kidney disease progression: Validation of the Klinrisk model in the CANVAS Program and CREDENCE trial.

AIM: To validate the Klinrisk machine learning model for prediction of chronic kidney disease (CKD) ...