Diabetes

Liver enzymes, alcohol consumption and the risk of diabetes: the Suita study.

AIM: We aimed to investigate the combined impact of liver enzymes and alcohol consumption on the diabetes risk.

Characterization of the SGLT2 Interaction Network and Its Regulation by SGLT2 Inhibitors: A Bioinformatic Analysis.

Sodium-glucose cotransporter 2 (SGLT2), also known as solute carrier family 5 member 2 (SLC5A2), is a promising target for a new class of drugs primarily established as kidney-targeting, effective glucose-lowering agents used in diabetes mellitus (DM) patients. Increasing evidence indicates that besides renal effects, SGLT2 inhibitors (SGLT2i) have also a systemic impact via indirectly targeting ...

A Deep Learning Model Incorporating Knowledge Representation Vectors and Its Application in Diabetes Prediction.

The deep learning methods for various disease prediction tasks have become very effective and even surpass human experts. However, the lack of interpr...

Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning.

The fovea centralis is an essential landmark in the retina where the photoreceptor layer is entirely composed of cones responsible for sharp, central ...

Semi-supervised classification of fundus images combined with CNN and GCN.

PURPOSE: Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which is a kind of fundus lesion with specific changes. Early...

A comprehensive review of methods based on deep learning for diabetes-related foot ulcers.

BACKGROUND: Diabetes mellitus (DM) is a chronic disease with hyperglycemia. If not treated in time, it may lead to lower limb amputation. At the initi...

Diagnosing Diabetic Retinopathy in OCTA Images Based on Multilevel Information Fusion Using a Deep Learning Framework.

OBJECTIVE: As an extension of optical coherence tomography (OCT), optical coherence tomographic angiography (OCTA) provides information on the blood f...

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection.

Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However...

Automated multidimensional deep learning platform for referable diabetic retinopathy detection: a multicentre, retrospective study.

OBJECTIVE: To develop and validate a real-world screening, guideline-based deep learning (DL) system for referable diabetic retinopathy (DR) detection...

Automated Prediction of Kidney Failure in IgA Nephropathy with Deep Learning from Biopsy Images.

BACKGROUND AND OBJECTIVES: Digital pathology and artificial intelligence offer new opportunities for automatic histologic scoring. We applied a deep l...

Identifying endotypes of individuals after an attack of pancreatitis based on unsupervised machine learning of multiplex cytokine profiles.

After an attack of pancreatitis, individuals may develop metabolic sequelae (eg, new-onset diabetes) and/or pancreatic cancer. These new-onset morbidi...

Drug Recommendation System for Diabetes Using a Collaborative Filtering and Clustering Approach: Development and Performance Evaluation.

BACKGROUND: Diabetes is a public health problem worldwide. Although diabetes is a chronic and incurable disease, measures and treatments can be taken ...

Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System.

BACKGROUND: With diabetes incidence growing globally and metformin still being the first-line for its treatment, metformin's toxicity and overdose hav...

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study.

Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes t...

Mortality Prediction Analysis among COVID-19 Inpatients Using Clinical Variables and Deep Learning Chest Radiography Imaging Features.

The emergence of the COVID-19 pandemic over a relatively brief interval illustrates the need for rapid data-driven approaches to facilitate clinical d...

Genetics and Epigenetics in Personalized Nutrition: Evidence, Expectations, and Experiences.

With the presentation of the blueprint of the first human genome in 2001 and the advent of technologies for high-throughput genetic analysis, personal...

Predicting poor glycemic control during Ramadan among non-fasting patients with diabetes using artificial intelligence based machine learning models.

AIMS: This study aims to predict poor glycemic control during Ramadan among non-fasting patients with diabetes using machine learning models.

Automated image curation in diabetic retinopathy screening using deep learning.

Diabetic retinopathy (DR) screening images are heterogeneous and contain undesirable non-retinal, incorrect field and ungradable samples which require...

Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models.

In this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of aug...

MB-SupCon: Microbiome-based Predictive Models via Supervised Contrastive Learning.

Human microbiome consists of trillions of microorganisms. Microbiota can modulate the host physiology through molecule and metabolite interactions. In...