Endocrinology

Diabetes

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

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REMED-T2D: A robust ensemble learning model for early detection of type 2 diabetes using healthcare dataset.

Early diagnosis and timely treatment of diabetes are critical for effective disease management and t...

Estimation of Hematocrit Volume Using Blood Glucose Concentration through Extreme Gradient Boosting Regressor Machine Learning Model.

Lifestyle diseases such as cardiovascular disorders, diabetes, etc. affect the physiological metabol...

Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study.

BACKGROUND: Modern lifestyle risk factors, like physical inactivity and poor nutrition, contribute t...

An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven network.

Diabetic Foot Ulcer (DFU) is a severe complication of diabetes mellitus, resulting in significant he...

A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions.

Cells are regulated at multiple levels, from regulations of individual genes to interactions across ...

[Use of Artificial Intelligence in Diabetic Retinopathy Screening: Experience in a Health Service in Santiago, Chile].

UNLABELLED: Early detection of diabetic retinopathy is critical for preventing vision loss.

PPARγ modulator predictor (PGMP_v1): chemical space exploration and computational insights for enhanced type 2 diabetes mellitus management.

Peroxisome proliferator-activated receptor gamma (PPARγ) plays a critical role in adipocyte differen...

Efficient diagnosis of retinal disorders using dual-branch semi-supervised learning (DB-SSL): An enhanced multi-class classification approach.

The early diagnosis of retinal disorders is essential in preventing permanent or partial blindness. ...

Prediction of insulin resistance using multiple adaptive regression spline in Chinese women.

Insulin resistance (IR) is the core for type 2 diabetes and metabolic syndrome. The homeostasis asse...

A review on retinopathy of prematurity.

BACKGROUND: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. It predomina...

Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling.

BACKGROUND: Type 1 diabetes (T1D) is a chronic endocrine disorder characterized by high blood glucos...

Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches.

BACKGROUND: The aim of this study was to evaluate the potential models to determine the most importa...

Optimal selection of diagnostic method for diabetes mellitus using complex bipolar fuzzy dynamic data.

Diabetes mellitus refers to a collection of metabolic disorders that affect the way carbohydrates ar...

Autonomous Screening for Diabetic Macular Edema Using Deep Learning Processing of Retinal Images.

OBJECTIVE: To develop and validate a deep learning model for diabetic macular edema (DME) detection ...

Cost-effectiveness of AI-based diabetic retinopathy screening in nationwide health checkups and diabetes management in Japan: A modeling study.

AIMS: We evaluated the cost-effectiveness of artificial intelligence (AI)-based diabetic retinopathy...

A deep learning based model for diabetic retinopathy grading.

Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR i...

Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification.

The classification of chronic diseases has long been a prominent research focus in the field of publ...

Risk factor assessment of prediabetes and diabetes based on epidemic characteristics in new urban areas: a retrospective and a machine learning study.

To explore in depth the characteristics of the risk factors for diabetes and prediabetes pathogenesi...

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...

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