Cardiovascular

Metabolic Syndrome

Latest AI and machine learning research in metabolic syndrome for healthcare professionals.

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In vitro, ex vivo and in vivo anti-hypertensive activity of Chrysophyllum cainito L. extract.

Chrysophyllum cainito L., a traditional herbal medicine, could have the potential for management of ...

Deficient serum 25-hydroxyvitamin D is associated with an atherogenic lipid profile: The Very Large Database of Lipids (VLDL-3) study.

BACKGROUND: Cross-sectional studies have found an association between deficiencies in serum vitamin ...

Coronary artery disease risk assessment from unstructured electronic health records using text mining.

Coronary artery disease (CAD) often leads to myocardial infarction, which may be fatal. Risk factors...

A Soft Computing Approach to Kidney Diseases Evaluation.

Kidney renal failure means that one's kidney have unexpectedly stopped functioning, i.e., once chron...

A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases.

Automated phenotype identification plays a critical role in cohort selection and bioinformatics data...

Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.

The second track of the 2014 i2b2/UTHealth natural language processing shared task focused on identi...

Apolipoprotein A-I: A Molecule of Diverse Function.

Apolipoprotein A-I (apo A-I) an indispensable component and a major structural protein of high-densi...

Using local lexicalized rules to identify heart disease risk factors in clinical notes.

Heart disease is the leading cause of death globally and a significant part of the human population ...

Serum tests, liver stiffness and artificial neural networks for diagnosing cirrhosis and portal hypertension.

BACKGROUND: The diagnostic performance of biochemical scores and artificial neural network models fo...

Identification of Type 2 Diabetes Risk Factors Using Phenotypes Consisting of Anthropometry and Triglycerides based on Machine Learning.

The hypertriglyceridemic waist (HW) phenotype is strongly associated with type 2 diabetes; however, ...

PhenoMiner: a quantitative phenotype database for the laboratory rat, Rattus norvegicus. Application in hypertension and renal disease.

Rats have been used extensively as animal models to study physiological and pathological processes i...

Refining in silico simulation to study digestion parameters affecting the bioaccessibility of lipophilic nutrients and micronutrients.

Despite the considerable number of in vivo and in vitro studies on the digestive fate of lipophilic ...

Identification of the Best Anthropometric Predictors of Serum High- and Low-Density Lipoproteins Using Machine Learning.

Serum high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol levels are associ...

Evaluation of insulin sensitivity temporal prediction by using quantile regression combined with neural network model.

BACKGROUND: Stress-induced hyperglycemia, a pathologically high blood glucose level, is a frequent c...

Early Identification of Vitamin D Deficiency Risk Through Public Health Screening Data.

Metabolic syndrome, characterized by central obesity, hypertension, hyperglycemia, dyslipidemia, and...

Automate Creating, Customizing, and Optimizing Comorbidity Indices Using a Data-Driven AI/ML Approach.

Due to individual differences in severity of illness, clinical studies typically use a comorbidity i...

Associations of the Hs-CRP/HDL-C ratio with stroke among US adults: Evidence from NHANES 2015-2018.

BACKGROUND: The high-sensitivity C-reactive protein (Hs-CRP)-to-high-density lipoprotein cholesterol...

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