Metabolic Syndrome

A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations.

Hypertension is a leading cause of mortality in the USA. While simple tools such as the sphygmomanometer are widely used to diagnose hypertension, they could not predict the disease before its onset. Clinical studies suggest that alterations in the structure of human brains' cerebrovasculature start to develop years before the onset of hypertension. In this research, we present a novel computer-ai...

Randomized Phase II Trial of Exercise, Metformin, or Both on Metabolic Biomarkers in Colorectal and Breast Cancer Survivors.

BACKGROUND: Observational data support inverse relationships between exercise or metformin use and disease outcomes in colorectal and breast cancer survivors, although the mechanisms underlying these associations are not well understood.

Statistical and machine learning methodology for abdominal aortic aneurysm prediction from ultrasound screenings.

A method of analysis of a database of patients (n = 10 329) screened for an abdominal aortic aneurysm (AAA) is presented. Self-reported height, weight...

A machine learning approach for the prediction of pulmonary hypertension.

BACKGROUND: Machine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we inve...

Machine learning of human plasma lipidomes for obesity estimation in a large population cohort.

Obesity is associated with changes in the plasma lipids. Although simple lipid quantification is routinely used, plasma lipids are rarely investigated...

Predicting diabetes second-line therapy initiation in the Australian population via time span-guided neural attention network.

INTRODUCTION: The first line of treatment for people with Diabetes mellitus is metformin. However, over the course of the disease metformin may fail t...

Machine learning and blood pressure.

Machine learning (ML) is a type of artificial intelligence (AI) based on pattern recognition. There are different forms of supervised and unsupervised...

Hyper-G: An Artificial Intelligence Tool for Optimal Decision-Making and Management of Blood Glucose Levels in Surgery Patients.

BACKGROUND: Hyperglycemia or high blood glucose during surgery is associated with poor postoperative outcome. Knowing in advance which patients may de...

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting.

For people with Type 1 diabetes (T1D), forecasting of blood glucose (BG) can be used to effectively avoid hyperglycemia, hypoglycemia and associated c...

On the interpretability of machine learning-based model for predicting hypertension.

BACKGROUND: Although complex machine learning models are commonly outperforming the traditional simple interpretable models, clinicians find it hard t...

TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records.

BACKGROUND: Insulin resistance is an early-stage deterioration of Type 2 diabetes. Identification and quantification of insulin resistance requires sp...

Disability in multiple sclerosis is associated with age and inflammatory, metabolic and oxidative/nitrosative stress biomarkers: results of multivariate and machine learning procedures.

The aim of this study was to evaluate the immune-inflammatory, metabolic, and nitro-oxidative stress (IM&NO) biomarkers as predictors of disability in...

Prediction of Aneurysm Stability Using a Machine Learning Model Based on PyRadiomics-Derived Morphological Features.

Background and Purpose- Discrimination of the stability of intracranial aneurysms is critical for determining the treatment strategy, especially in sm...

Minimizing postprandial hypoglycemia in Type 1 diabetes patients using multiple insulin injections and capillary blood glucose self-monitoring with machine learning techniques.

BACKGROUND: Diabetic patients treated with intensive insulin therapies require a tight glycemic control and may benefit from advanced tools to predict...

Identifying pre-disease signals before metabolic syndrome in mice by dynamical network biomarkers.

The establishment of new therapeutic strategies for metabolic syndrome is urgently needed because metabolic syndrome, which is characterized by severa...

Modelling disease risk for amyloid A (AA) amyloidosis in non-human primates using machine learning.

Amyloid A (AA) amyloidosis is found in humans and non-human primates, but quantifying disease risk prior to clinical symptoms is challenging. We appl...

Computer-Aided Diagnosis and Clinical Trials of Cardiovascular Diseases Based on Artificial Intelligence Technologies for Risk-Early Warning Model.

The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysi...

Instant White Rice with Pigmented Giant Embryonic Rice Improves Glucose Metabolism and Inhibits Oxidative Stress in High-Fat Diet-Fed Mice.

The effects of instant cooked rice made from a combination of white rice and pigmented giant embryonic Keunnunjami rice, in comparison with those of i...

A Precision Environment-Wide Association Study of Hypertension via Supervised Cadre Models.

We consider the problem in precision health of grouping people into subpopulations based on their degree of vulnerability to a risk factor. These subp...

Directional Relationship Between Vitamin D Status and Prediabetes: A New Approach from Artificial Neural Network in a Cohort of Workers with Overweight-Obesity.

Despite the increasing literature on the association of diabetes with inflammation, cardiovascular risk, and vitamin D (25(OH)D) concentrations, stro...