Diabetes

Multimodal deep learning applied to classify healthy and disease states of human microbiome.

Metagenomic sequencing methods provide considerable genomic information regarding human microbiomes, enabling us to discover and understand microbial diseases. Compositional differences have been reported between patients and healthy people, which could be used in the diagnosis of patients. Despite significant progress in this regard, the accuracy of these tools needs to be improved for applicatio...

Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture.

Diabetes intensive care unit (ICU) patients are at increased risk of complications leading to in-hospital mortality. Assessing the likelihood of death is a challenging and time-consuming task due to a large number of influencing factors. Healthcare providers are interested in the detection of ICU patients at higher risk, such that risk factors can possibly be mitigated. While such severity scoring...

Application of a Machine Learning Technology in the Definition of Metabolically Healthy and Unhealthy Status: A Retrospective Study of 2567 Subjects Suffering from Obesity with or without Metabolic Syndrome.

The key factors playing a role in the pathogenesis of metabolic alterations observed in many patients with obesity have not been fully characterized. ...

A Dielectric Elastomer-Based Multimodal Capacitive Sensor.

Dielectric elastomer (DE) sensors have been widely used in a wide variety of applications, such as in robotic hands, wearable sensors, rehabilitation ...

A deep learning model for screening type 2 diabetes from retinal photographs.

BACKGROUND AND AIMS: We aimed to develop and evaluate a non-invasive deep learning algorithm for screening type 2 diabetes in UK Biobank participants ...

Predicting the evolution of number of native contacts of a small protein by using deep learning approach.

Native contacts (NCs) are one of the most vital parameters in order to define the resemblance of a protein conformation with its native state. Predict...

Connection between the Plasma Level of Apelin and Diabetic Nephropathy in Type 2 Diabetic Patients. A Case Control Study.

BACKGROUND: Apelin is an adipokine that may have an advantageous role in the prediction of early diabetic nephropathy. A few studies on apelin in diab...

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology.

BACKGROUND: Untargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our un...

Deep Learning Approach for Automatic Microaneurysms Detection.

In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shap...

A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques.

Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population's health. It is a metabolic...

Classifying central serous chorioretinopathy subtypes with a deep neural network using optical coherence tomography images: a cross-sectional study.

Central serous chorioretinopathy (CSC) is the fourth most common retinopathy and can reduce quality of life. CSC is assessed using optical coherence t...

A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems.

Current artificial pancreas (AP) systems are hybrid closed-loop systems that require manual meal announcements to manage postprandial glucose control ...

Deep CNN with Hybrid Binary Local Search and Particle Swarm Optimizer for Exudates Classification from Fundus Images.

Diabetic retinopathy is a chronic condition that causes vision loss if not detected early. In the early stage, it can be diagnosed with the aid of exu...

Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans.

Diabetes is a serious metabolic disorder with high rate of prevalence worldwide; the disease has the characteristics of improper secretion of insulin ...

Preoperative oral carbohydrate loading: Effects on intraoperative blood glucose levels, post-operative nausea and vomiting, and intensive care unit stay.

BACKGROUND AND AIMS: Preoperative fasting imparts safety to patients from aspiration of gastric contents, but undue fasting may predispose to hypoglyc...

Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs: A Multicenter Validation Study.

PURPOSE: To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from 2-dimensional color fundus ph...

Deep learning-based pancreas volume assessment in individuals with type 1 diabetes.

Pancreas volume is reduced in individuals with diabetes and in autoantibody positive individuals at high risk for developing type 1 diabetes (T1D). St...

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 get up, and had been lying on the floor for rough...

Attenuation of Oxidative Stress, Interleukin-6, High-Sensitivity C-Reactive Protein, Plasminogen Activator Inhibitor-1, and Fibrinogen with Oral Vitamin D Supplementation in Patients with T2DM having Vitamin D Deficiency.

 Type 2 diabetes mellitus (T2DM) associated with oxidative stress and inflammation causes endothelial dysfunction, which promotes cardiovascular risk...

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 thromboembolic stroke risk and heart failure. Although variou...