Dyslipidemia

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 can be used to predict CAD, which may subsequently lead to prevention or early intervention. Patient data such as co-morbidities, medication history, social history and family history are required to determine the risk factors for a disease. However, risk factor data are usually embedded in unstruc...

Combination of counterpropagation artificial neural networks and antioxidant activities for comprehensive evaluation of associated-extraction efficiency of various cyclodextrins in the traditional Chinese formula Xue-Zhi-Ning.

Xue-Zhi-Ning (XZN) is a widely used traditional Chinese medicine formula to treat hyperlipidemia. Recently, cyclodextrins (CDs) have been extensively used to minimize problems relative to medicine bioavailability, such as low solubility and poor stability. The objective of this study was to determine the associated-extraction efficiency of various CDs in XZN. Three various type CDs were evaluated,...

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 mining. Natural Language Processing (NLP)-informe...

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 identifying medical risk factors related to Coronary Art...

Apolipoprotein A-I: A Molecule of Diverse Function.

Apolipoprotein A-I (apo A-I) an indispensable component and a major structural protein of high-density lipoprotein (HDL), plays a vital role in revers...

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 lives with it. A number of risk factors have been ...

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 Ramadan have not been studied in Singapore. This st...

The potential role of an expert computer system to augment the opportunistic detection of individuals with familial hypercholesterolaemia from a community laboratory.

BACKGROUND: Familial hypercholesterolaemia (FH) is the most common monogenic cause of premature atherosclerotic cardiovascular disease (CVD). However,...

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, to date, no study has assessed the predictive powe...

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 nutrients, micronutrients, and bioactives, the eff...

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 atherosclerosis. The aim of the study was to asse...

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 associated with risk factors for various diseases and ar...

ImageCAS-X: a dataset and benchmark for coronary artery segmentation and centerline extraction in coronary CT angiography

Accurate segmentation of the coronary vessel lumen is a prerequisite for quantitative assessment of atherosclerotic plaque and perivascular adipose ti...

Performance, Generalizability, and Fairness of a Peripheral Artery Disease Detection Model Across Patient Phenotypes and Health Systems

Background Peripheral artery disease (PAD) is a major cause of cardiovascular events but remains underdiagnosed. Electronic health record (EHR)-based ...

Geographically Weighted Machine Learning for Spatial Prediction of Cancer Prevalence in the United States: A Mixed Method Approach

Cancer prevalence is distributed unevenly across regions and caused by the interaction of multiple risk factors. Previous studies focused on the use o...

A Vision-Language Model for Coronary Angiography Interpretation and Clinical Decision Support

BACKGROUND: Coronary angiography remains the reference standard for diagnosing coronary artery disease and guiding revascularization, yet its interpre...

A Deep Learning-Derived Insulin Resistance Index for Cardiovascular Risk Prediction: A Prospective Cohort Study with External Validation in Chinese and US Populations

Background Existing insulin resistance (IR) indices are predominantly developed in diabetic cohorts, limiting their generalizability. We developed a n...

GenFAR: A generalized representation of brain structure, derived from 49,246 multi-cohort MRIs via deep learning

Deep learning models for neuroimaging have largely been developed for individual tasks, limiting knowledge transfer across applications. Here we intro...

Genetic decoding reveals druggable biology implicitly learned by a medical-history foundation model

Foundation models trained on electronic healthcare records (EHRs) have gained traction with the aim to transform personalised medicine. However, their...

AI-Enabled Echocardiography Identifies an Adverse Epicardial Adiposity Phenotype Associated with Cardiometabolic Dysfunction

Background: Excess epicardial adipose tissue (EAT) is associated with cardiovascular-kidney-metabolic (CKM) dysfunction, but its assessment has tradit...