Metabolic Syndrome

Logistic Regression and Statistical Regularization Techniques for Risk Classification of Coronary Artery Disease using Cytokines transported by high density lipoproteins.

Coronary artery disease (CAD) is a leading cause of mortality in the world. It is important to be able to proactively assess the risk of the disease, using novel biomarkers like cytokines that are indicators of inflammation in addition to traditional predictors of risk. Atherosclerosis, the primary cause of CAD, is an inflammatory disease involving cytokines. Identifying which cytokines are specif...

A novel optimization-assisted multi-scale and dilated adaptive hybrid deep learning network with feature fusion for event detection from social media.

Social media networks become an active communication medium for connecting people and delivering new messages. Social media can perform as the primary channel, where the globalized events or instances can be explored. Earlier models are facing the pitfall of noticing the temporal and spatial resolution for enhancing the efficacy. Therefore, in this proposed model, a new event detection approach fr...

Brief Review and Primer of Key Terminology for Artificial Intelligence and Machine Learning in Hypertension.

Recent breakthroughs in artificial intelligence (AI) have caught the attention of many fields, including health care. The vision for AI is that a comp...

Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report.

Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (A...

A machine learning algorithm for stratification of risk of cardiovascular disease in metabolic dysfunction-associated steatotic liver disease.

BACKGROUND: Steatotic liver disease (SLD) is associated with adverse cardiac events. Metabolic dysfunction-associated steatotic liver disease (MASLD) ...

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods.

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mor...

A transparent machine learning algorithm uncovers HbA1c patterns associated with therapeutic inertia in patients with type 2 diabetes and failure of metformin monotherapy.

AIMS: This study aimed to identify and categorize the determinants influencing the intensification of therapy in Type 2 Diabetes (T2D) patients with s...

A Novel Implementation of a Social Robot for Sustainable Human Engagement in Homecare Services for Ageing Populations.

This research addresses the rapid aging phenomenon prevalent in Asian societies, which has led to a significant increase in elderly individuals reloca...

Applications of artificial intelligence in computed tomography imaging for phenotyping pulmonary hypertension.

PURPOSE OF REVIEW: Pulmonary hypertension is a heterogeneous condition with significant morbidity and mortality. Computer tomography (CT) plays a cent...

Artificial intelligence derived categorizations significantly improve HOMA IR/β indicators: Combating diabetes through cross-interacting drugs.

Improvements in the homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of beta-cell function (HOMA-β) signi...

Predicting dyslipidemia incidence: unleashing machine learning algorithms on Lifestyle Promotion Project data.

BACKGROUND: Dyslipidemia, characterized by variations in plasma lipid profiles, poses a global health threat linked to millions of deaths annually.

Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening.

Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluids, offering a promising avenue for high-throughput...

A Novel AI Approach for Assessing Stress Levels in Patients with Type 2 Diabetes Mellitus Based on the Acquisition of Physiological Parameters Acquired during Daily Life.

Stress is the inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic conditi...

Supervised Machine Learning-Based Models for Predicting Raised Blood Sugar.

Raised blood sugar (hyperglycemia) is considered a strong indicator of prediabetes or diabetes mellitus. Diabetes mellitus is one of the most common n...

Identification of immune-related genes and small-molecule drugs in hypertension-induced left ventricular hypertrophy based on machine learning algorithms and molecular docking.

BACKGROUND: Left ventricular hypertrophy (LVH) is a common consequence of hypertension and can lead to heart failure. The immune response plays an imp...

Causal prior-embedded physics-informed neural networks and a case study on metformin transport in porous media.

This study introduces a novel approach to transport modelling by integrating experimentally derived causal priors into neural networks. We illustrate ...

Unveiling the Role of Artificial Intelligence (AI) in Polycystic Ovary Syndrome (PCOS) Diagnosis: A Comprehensive Review.

Polycystic Ovary Syndrome (PCOS) is one of the most widespread endocrine and metabolic disorders affecting women of reproductive age. Major symptoms i...

Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring.

Hypertension, a widespread cardiovascular issue, presents a major global health challenge. Traditional diagnosis and treatment methods involve periodi...

Microscopy Image Dataset for Deep Learning-Based Quantitative Assessment of Pulmonary Vascular Changes.

Pulmonary hypertension (PH) is a syndrome complex that accompanies a number of diseases of different etiologies, associated with basic mechanisms of s...

Development and validation of a machine learning-based readmission risk prediction model for non-ST elevation myocardial infarction patients after percutaneous coronary intervention.

To investigate the factors that influence readmissions in patients with acute non-ST elevation myocardial infarction (NSTEMI) after percutaneous coron...