Congestive Heart Failure

Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases.

N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP) is important for diagnosing and predicting heart failure or many other diseases. However, few studies have comprehensively assessed the factors correlated with NT-proBNP levels in people with cardiovascular health. We used data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES). Machine learning was employed to assess 6...

Is artificial intelligence superior to traditional regression methods in predicting prognosis of adult traumatic brain injury?

Traumatic brain injury (TBI) is a significant global health issue with high morbidity and mortality rates. Recent studies have shown that machine learning algorithms outperform traditional logistic regression models in predicting functional outcomes for TBI patients. This research aimed to compare the accuracy of the binomial logistic regression model with the Extreme Gradient Boosting (XGBoost) m...

Safety assessment of the ethanolic extract of Siparuna guianensis: Cell viability, molecular risk predictions and toxicity risk for acute and sub-chronic oral ingestion.

ETHNOPHARMACOLOGICAL RELEVANCE: The species Siparuna guianensis Aublet (family Siparunaceae) is traditionally used by indigenous peoples and riverine ...

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 ex...

Comment on "Machine learning risk-prediction model for in-hospital mortality in Takotsubo cardiomyopathy".

Artificial Intelligence-Enhanced Analysis of Echocardiography-Based Radiomic Features for Myocardial Hypertrophy Detection and Etiology Differentiation.

BACKGROUND: While echocardiography is pivotal for detecting left ventricular hypertrophy (LVH), it struggles with etiology differentiation. To enhance...

Construction and validation of a predictive model for intracardiac thrombus risk in patients with dilated cardiomyopathy: a retrospective study.

BACKGROUND: Systemic embolic events due to exfoliation of intracardiac thrombus (ICT) are one of the catastrophic complications of dilated cardiomyopa...

Predictive power of artificial intelligence for malignant cerebral edema in stroke patients: a CT-based systematic review and meta-analysis of prevalence and diagnostic performance.

Malignant cerebral edema (MCE) is a severe complication of acute ischemic stroke, with high mortality rates. Early and accurate prediction of MCE is c...

Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning.

BACKGROUND: Diabetic cardiomyopathy (DC) is a serious complication in patients with type 1 diabetes mellitus and has become a growing public health pr...

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...

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 ...

CPHNet: a novel pipeline for anti-HAPE drug screening via deep learning-based Cell Painting scoring.

BACKGROUND: High altitude pulmonary edema (HAPE) poses a significant medical challenge to individuals ascending rapidly to high altitudes. Hypoxia-ind...

Construction and validation of a predictive model for meningoencephalitis in pediatric scrub typhus based on machine learning algorithms.

To retrospectively analyze the clinical characteristics of pediatric scrub typhus (ST) with meningoencephalitis (STME) and to construct and validate p...

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...

Performance of a Deep Learning Diabetic Retinopathy Algorithm in India.

IMPORTANCE: While prospective studies have investigated the accuracy of artificial intelligence (AI) for detection of diabetic retinopathy (DR) and di...

Interpretation of cardiopulmonary exercise test by GPT - promising tool as a first step to identify normal results.

BACKGROUND: Cardiopulmonary exercise testing (CPET) is used in the evaluation of unexplained dyspnea. However, its interpretation requires expertise t...

Deep Learning-Assisted Diagnosis of Malignant Cerebral Edema Following Endovascular Thrombectomy.

BACKGROUND: Malignant cerebral edema (MCE) is a significant complication following endovascular thrombectomy (EVT) in the treatment of acute ischemic ...

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...