Endocrinology

Diabetes

Latest AI and machine learning research in diabetes for healthcare professionals.

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Rhabdomyolysis: An evidence-based approach.

A 76-year-old lady was found on the floor following a fall at home. She was uninjured, but unable to...

Explainable Machine Learning for Atrial Fibrillation in the General Population Using a Generalized Additive Model - A Cross-Sectional Study.

Atrial fibrillation (AF) is the most common arrhythmia and is associated with increased thromboembo...

Data Homogeneity Effect in Deep Learning-Based Prediction of Type 1 Diabetic Retinopathy.

This study is aimed at evaluating a deep transfer learning-based model for identifying diabetic reti...

Risk Assessment and Determination of Factors That Cause the Development of Hyperinsulinemia in School-Age Adolescents.

: Hyperinsulinemia and insulin resistance are not synonymous; if the risk of developing insulin resi...

Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing.

The measurement and quantification of glucose concentrations is a field of major interest, whether m...

Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients.

Continuous monitoring of blood glucose (BG) levels is a key aspect of diabetes management. Patients ...

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.

Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facet...

Elevated Levels of Urinary Biomarkers TIMP-2 and IGFBP-7 Predict Acute Kidney Injury in Neonates after Congenital Heart Surgery.

 This article investigated the utility of urine biomarkers tissue inhibitor of metalloproteinase-2 ...

Native BK virus nephropathy in lung transplant: a case report and literature review.

Classically described in renal allografts, BK virus nephropathy is increasingly recognized in native...

Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification.

Pathologists use multiple microscopy modalities to assess renal biopsy specimens. Besides usual diag...

A stratified analysis of a deep learning algorithm in the diagnosis of diabetic retinopathy in a real-world study.

BACKGROUND: The aim of our research was to prospectively explore the clinical value of a deep learni...

A Comparative Performance Assessment of Optimized Multilevel Ensemble Learning Model with Existing Classifier Models.

To predict the class level of any classification problem, predictive models are used and mostly a si...

Weakly Supervised Sensitive Heatmap framework to classify and localize diabetic retinopathy lesions.

Vision loss happens due to diabetic retinopathy (DR) in severe stages. Thus, an automatic detection ...

Systems biology and machine learning approaches identify drug targets in diabetic nephropathy.

Diabetic nephropathy (DN), the leading cause of end-stage renal disease, has become a massive global...

Corticosteroids and mycophenolic acid analogues in immunoglobulin A nephropathy with progressive decline in kidney function.

BACKGROUND: A randomized controlled trial demonstrated a beneficial effect of corticosteroids (CS) +...

A weakly supervised model for the automated detection of adverse events using clinical notes.

With clinical trials unable to detect all potential adverse reactions to drugs and medical devices p...

A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography.

As the prevalence of diabetes increases, millions of people need to be screened for diabetic retinop...

Phytogenic compounds from avocado ( L.) extracts; antioxidant activity, amylase inhibitory activity, therapeutic potential of type 2 diabetes.

Diabetes is a worldwide public health disease. Currently, the most effective way to treat diabetes i...

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