Congestive Heart Failure

Characteristics, cost/effect consideration of clinical examinations, and construction of machine learning models of restrictive cardiomyopathy: insights from Peking Union Medical College Hospital.

Identifying a fatty acid metabolic gene signature in diabetic cardiomyopathy through integrated bioinformatics and machine learning.

BACKGROUND: Diabetic cardiomyopathy (DCM) is a major cardiovascular complication of diabetes mellitus, ultimately progressing to heart failure and increased mortality. Its pathogenesis involves multifactorial mechanisms, and the key causative genes remain to be fully elucidated. METHODS: We merged and batch-corrected seven Gene Expression Omnibus (GEO) datasets. A combination of differential expre...

Conversational AI for remote monitoring in heart failure: a prospective controlled pilot study.

AIMS: Heart failure (HF) requires scalable strategies to detect decompensation early and reduce hospitalizations. Existing telemonitoring tools are of...

Abnormal Lipid Signaling Characterizes Diastolic Dysfunction in Pediatric Cardiomyopathy.

Pediatric cardiomyopathy is a major cause of diastolic heart failure; yet, its mechanisms remain unclear. Global myocardial transcriptomic and blood l...

Investigating feature-engineered predictors for systolic blood pressure changes in an mHealth-based disease management program.

Mobile health (mHealth)-based disease management programs enable continuous monitoring of blood pressure (BP) and related health behaviors. Feature en...

Application of machine learning approaches to develop predictive models for diabetes and hypertension among Bangladeshi Adults.

With rapid urbanization, lifestyle changes, and an aging population, non-communicable diseases (NCDs), including hypertension and diabetes, pose signi...

PANI E-skin Inspired by Microridge Structure and the Collaborative Mechanism of Mechanoreceptors for Elderly Health Monitoring and Intelligent Recognition of Abnormal Plantar Pressures.

Human skin, with its microridge structure and synergistic mechanoreceptors, precisely perceives stimuli from touch to high pressures. Inspired by this...

Detection and prognostic stratification of left ventricular systolic dysfunction in left bundle branch block using an artificial intelligence-enabled electrocardiography.

BACKGROUND: Left bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchro...

Early prediction of transfusion requirements in trauma patients using explainable machine learning.

INTRODUCTION: Hemorrhagic shock is the most common preventable cause of death in trauma patients. Early transfusion significantly improves survivabili...

Personalized sepsis mortality prediction: An interpretable machine learning nomogram.

PURPOSE: Sepsis remains a major cause of mortality in ICU patients, requiring accurate prognostic tools for optimal management. This study aimed to de...

Robotic-Arm Assisted Multi-Apical View 3-D Fusion of Echocardiography for Enhanced Left Ventricular Assessment Using Wavelet.

OBJECTIVE: Accurate assessment of left ventricular (LV) function using three-dimensional echocardiography (3-DE) remains limited by suboptimal image q...

A lightweight residual dilated temporal transformer block for ECG classification on edge devices.

Wearable devices play a crucial role in healthcare by enabling continuous monitoring of vital physiological signals such as ECG, heart rate, respirati...

Retinal Biomarkers for Cardiovascular Disease Prediction: A Review Focused on CHD AHD Valvular Disorders and Cardiomyopathies.

INTRODUCTION: Cardiovascular diseases (CVDs) remain the leading cause of global mortality, with congenital heart disease (CHD), acquired heart disease...

Diagnosis of Cardiac Amyloidosis on Echocardiography Using Artificial Intelligence.

BACKGROUND: Diagnosing cardiac amyloidosis (CA) on echocardiography can be challenging due to the imaging overlap between CA and more prevalent causes...

Diagnostic model and identification of IGFBP2 as an aging-related biomarker and therapeutic target for systemic sclerosis-associated interstitial lung disease through integrated bioinformatics and machine learning approaches.

BACKGROUND: Cellular senescence is a critical contributor to the pathogenesis of systemic sclerosis-associated interstitial lung disease (SSc-ILD). Ho...

Synthetic, Population-Based Virtual Patient Database Using a Digital Twin of the Cardiovascular System.

PURPOSE: The goal is to develop a cardiovascular virtual patient database (VPD) combining physiological and demographic data to provide the foundation...

Machine learning-based on model for explain risk of 24-hour death in critically ill patients in the prehospital setting: A retrospective cohort study.

This study aimed to develop and validate a machine learning-based model for predicting 24-hour mortality in critically ill patients using prehospital ...

From Observation to Prediction: Machine Learning Analysis of Progression of Visual loss in Nonarteritic Anterior Ischemic Optic Neuropathy.

OBJECTIVE: To determine whether combinations of modifiable clinical/systemic risk factors and structured trial variables predict early disease progres...

TriFTM-Net: Tri-Path Fourier-Temporal Modulation Network for macular edema pathology segmentation and reconstruction in high-precision intraoperative navigation.

Ophthalmic diseases such significantly impair the vision of numerous individuals globally. Accurate and real-time 3D reconstruction of macular edema a...

Data-driven subgroups for 3-year risk stratification of incident diabetes and complications in diabetes-free Chinese adults.

BACKGROUND: Substantial metabolic heterogeneity exists prior to the development of diabetes, creating opportunities for earlier and more precise inter...