Cardiovascular

Dyslipidemia

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

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Toward clearer recognition and easier usefulness: development of a cross-lingual atherosclerotic cerebrovascular disease ontology.

Atherosclerotic cerebrovascular disease could result in a great number of deaths and disabilities. H...

Incremental modelling and analysis of biological systems with fuzzy hybrid Petri nets.

Modelling biological systems depends on the availability of data and components of the system at han...

Primary care research on hypertension: A bibliometric analysis using machine-learning.

Hypertension is one of the most important chronic diseases worldwide. Hypertension is a critical con...

Deep Learning and Single-Cell Sequencing Analyses Unveiling Key Molecular Features in the Progression of Carotid Atherosclerotic Plaque.

Rupture of advanced carotid atherosclerotic plaques increases the risk of ischaemic stroke, which ha...

Shaping the future of heart health.

For World Heart Day on September 24, 2024, the World Heart Federation urges nations to endorse natio...

Multimodal ischemic stroke recurrence prediction model based on the capsule neural network and support vector machine.

Ischemic stroke (IS) has a high recurrence rate. Machine learning (ML) models have been developed ba...

Exploring Prediabetes Pathways Using Explainable AI on Data from Electronic Medical Records.

This study leverages data from a Canadian database of primary care Electronic Medical Records to dev...

Bioinformatics and machine learning approaches reveal key genes and underlying molecular mechanisms of atherosclerosis: A review.

Atherosclerosis (AS) causes thickening and hardening of the arterial wall due to accumulation of ext...

Prediction of low-density lipoprotein cholesterol levels using machine learning methods.

OBJECTIVE: Low-density lipoprotein cholesterol (LDL-C) has been commonly calculated by equations, bu...

Predicting Diabetes in Canadian Adults Using Machine Learning.

Rising diabetes rates have led to increased healthcare costs and health complications. An estimated ...

DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies.

Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) ...

Comparison of machine learning models to predict complications of bariatric surgery: A systematic review.

Due to changes in lifestyle, bariatric surgery is expanding worldwide. However, this surgery has nu...

A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion.

BACKGROUND: Diseases are medical situations that are allied with specific signs and symptoms. A dise...

Deep Learning Models for Coronary Atherosclerosis Detection in Coronary CT Angiography.

BACKGROUND: Patients with atherosclerosis have a rather high risk of showing complications, if not d...

H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA.

2'-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a cruci...

DETERMINATION OF THE PRA POSITIVITY PERCENTAGE IN MALE PATIENTS WITH CHRONIC KIDNEY DISEASE BY USING FLOW CYTOMETRY TECHNIQUE.

The antibodies directed against human leukocyte antigen (HLA) molecules, which play a crucial role i...

Assessing the Generalizability of a Deep Learning-based Automated Atrial Fibrillation Algorithm.

Automated detection of atrial fibrillation (AF) from electrocardiogram (ECG) traces remains a challe...

SmartWoodID-an image collection of large end-grain surfaces to support wood identification systems.

Wood identification is a key step in the enforcement of laws and regulations aimed at combatting ill...

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