Hypertension

Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning.

The burden of diabetic foot ulcers (DFU) is exacerbated in diabetic patients with concomitant arteriosclerotic occlusion disease (ASO) in the lower extremities, who experience more severe symptoms and poorer prognoses. The study aims to develop a predictive model grounded in machine learning (ML) algorithms, specifically tailored to forecast the occurrence of DFU in diabetic patients with lower ex...

Gender Differences in Predicting Metabolic Syndrome Among Hospital Employees Using Machine Learning Models: A Population-Based Study.

BACKGROUND: Metabolic syndrome (MetS) is a complex condition that captures several markers of dysregulation, including obesity, elevated blood glucose levels, dyslipidemia and hypertension. Using an approach to early prediction of MetS risk in hospital employees that takes into account the differing effects of gender may be expected to improve cardiovascular disease-related health outcomes.

Machine learning combined with infrared spectroscopy for detection of hypertension pregnancy: towards newborn and pregnant blood analysis.

Biochemical changes in the cervix during labor are not well understood. This gap in knowledge is significant, as understanding the precise biochemical...

Which approach better predicts diabetes: Traditional econometric methods or machine learning? Evidence from a cross-sectional study in South Korea.

To prevent chronic disease from getting worse, it is important to detect and predict it at an early stage. Therefore, the accuracy of the prediction i...

Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals.

Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic...

Predicting intra-abdominal hypertension using anthropometric measurements and machine learning.

Almost one in four critically ill patients suffer from intra-abdominal hypertension (IAH). Currently, the gold standard for measuring intra-abdominal ...

Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management.

Hypertension presents the largest modifiable public health challenge due to its high prevalence, its intimate relationship to cardiovascular diseases,...

Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations.

This study aimed to explore the combined impacts of occupational noise and dust on hearing and extra-auditory functions and identify associated risk f...

Tlalpan 2020 Case Study: Enhancing Uric Acid Level Prediction with Machine Learning Regression and Cross-Feature Selection.

Uric acid is a key metabolic byproduct of purine degradation and plays a dual role in human health. At physiological levels, it acts as an antioxidan...

Developing a machine learning-based predictive model for levothyroxine dosage estimation in hypothyroid patients: a retrospective study.

Hypothyroidism, a common endocrine disorder, has a high incidence in women and increases with age. Levothyroxine (LT4) is the standard therapy; howeve...

Machine learning prediction of right ventricular volume and ejection fraction from two-dimensional echocardiography in patients with pulmonary regurgitation.

Right ventricular (RV) end-diastolic volume (RVEDV) and ejection fraction (RVEF) by cardiac MRI (cMRI) guide management in chronic pulmonary regurgita...

Deep learning-based automated segmentation of cardiac real-time MRI in non-human primates.

Advanced imaging techniques, like magnetic resonance imaging (MRI), have revolutionised cardiovascular disease diagnosis and monitoring in humans and ...

A machine learning approach to predict mortality and neonatal persistent pulmonary hypertension in newborns with congenital diaphragmatic hernia. A retrospective observational cohort study.

UNLABELLED: Congenital diaphragmatic hernia (CDH) has high morbidity and mortality rates. This study aimed to develop a machine learning (ML) algorith...

Prediction of Hypertension in the Pediatric Population Using Machine Learning and Transfer Learning: A Multicentric Analysis of the SAYCARE Study.

OBJECTIVE: To develop a machine learning (ML) model utilizing transfer learning (TL) techniques to predict hypertension in children and adolescents ac...

Intelligent risk stratification of hypertension based on ambulatory blood pressure monitoring and machine learning algorithms.

. Risk stratification of hypertension plays a crucial role in the treatment decisions and medication guidance during clinical practices. Although frui...

Predicting Cardiac Magnetic Resonance-Derived Ejection Fraction from Echocardiogram Via Deep Learning Approach in Tetralogy of Fallot.

Systolic function assessment is essential in children with congenital heart disease. Traditional methods of echocardiographic left ventricular ejectio...

Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning.

BACKGROUND: Metabolic diseases (MDs), exemplified by diabetes, hypertension, and dyslipidemia, have become increasingly prevalent with rising living s...

Prediction of contrast-associated acute kidney injury with machine-learning in patients undergoing contrast-enhanced computed tomography in emergency department.

Radiocontrast media is a major cause of nephrotoxic acute kidney injury(AKI). Contrast-enhanced CT(CE-CT) is commonly performed in emergency departmen...

A spectral machine learning approach to derive central aortic pressure waveforms from a brachial cuff.

Analyzing cardiac pulse waveforms offers valuable insights into heart health and cardiovascular disease risk, although obtaining the more informative ...

Natural language processing of electronic medical records identifies cardioprotective agents for anthracycline induced cardiotoxicity.

In this retrospective observational study, we aimed to investigate the potential of natural language processing (NLP) for drug repositioning by analyz...