Cardiovascular

Dyslipidemia

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

4,912 articles
Stay Ahead - Weekly Dyslipidemia research updates
Subscribe
Browse Specialties
Showing 589-609 of 4,912 articles
Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach.

The number of the overweight people continues to rise across the world. Studies have shown that bein...

Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution.

Clinical studies model the average treatment effect (ATE), but apply this population-level effect to...

Characterization of the abnormal lipid profile in Chinese patients with psoriasis.

Psoriasis is a chronic inflammatory skin disease that has been associated with abnormal lipid metabo...

Gingipains from Porphyromonas gingivalis promote the transformation and proliferation of vascular smooth muscle cell phenotypes.

The aim of the present study was to ascertain the effect of Porphyromonas gingivalis cysteine protea...

A context-aware approach for progression tracking of medical concepts in electronic medical records.

Electronic medical records (EMRs) for diabetic patients contain information about heart disease risk...

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 ...

A bifurcation identifier for IV-OCT using orthogonal least squares and supervised machine learning.

Intravascular optical coherence tomography (IV-OCT) is an in-vivo imaging modality based on the intr...

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...

Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes.

The 2014 i2b2 natural language processing shared task focused on identifying cardiovascular risk fac...

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 ...

Metabolic parameters in type 2 diabetic patients with varying degrees of glycemic control during Ramadan: An observational study.

AIMS/INTRODUCTION: The changes in metabolic parameters in type 2 diabetic patients who fast during R...

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, ...

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 ...

Variability of automated carotid intima-media thickness measurements by novice operators.

Carotid intima-media thickness (C-IMT) measurements provide a non-invasive assessment of subclinical...

Browse Specialties