Hypertension

Personalised approach to hypertension treatment: Rationale and design of the HYPERMARKER randomised trial

Background and Objective: Blood pressure treatment response is variable in individual patients, and the choice of medical therapy is often dependent on clinician experience. Treatment choices can be personalised by patient empowerment, metabolomic profiles and augmented by machine learning, but robust evaluation is lacking on how these can be combined to enhance clinical effectiveness. The HYPERMA...

Serum Proteomic Profiling Implicates a Dysregulated Neurohormonal-Inflammatory Axis in Post-Fontan Tachycardia

Background: Post-operative tachycardia is a common and poorly understood complication following the Fontan procedure. Post-operative factors such as surgical scarring and venous hypertension can contribute to tachycardia risk, but the specific molecular signaling cascades triggering acute tachycardia remain uncharacterized, limiting therapeutic innovation and leaving clinicians with limited strate...

Machine Learning Identifies Distinct Treg-Mediated Remodeling in HFpEF Hearts Treated with Neonatal Mesenchymal Stem Cells and Their Secretome

Background: Heart failure with preserved ejection fraction (HFpEF) remains a major therapeutic challenge due to its complex pathophysiology and pronou...

Gene-exposure interactions regulate cytokine-mediated chronic inflammation and cardiac remodeling

Background: Chronic inflammation predicts adverse cardiovascular outcomes, but mechanisms linking systemic inflammation to cardiac remodeling remain i...

Artificial Intelligence-Enabled Echocardiographic Assessment of Right Ventricular Function

Background: Right ventricular (RV) function is an important predictor of morbidity and mortality in various cardiovascular conditions. Nevertheless, i...

Using Artificial Intelligence to Assess Treatment-Effect Heterogeneity in Pragmatic Cardiovascular Trials: Insights from TRANSFORM-HF

Background and Aims: Pragmatic clinical trials are designed to assess interventions in real-world settings, and their broad inclusion criteria and cli...

LIBRA: Language Model Informed Bandit Recourse Algorithm for Personalized Treatment Planning

We introduce a unified framework that seamlessly integrates algorithmic recourse, contextual bandits, and large language models (LLMs) to support sequ...

Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers.

Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for early risk prediction. This study aimed to develop...

SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations

The emergence of diffusion models has significantly advanced generative AI, improving the quality, realism, and creativity of image and video genera...

MTCNet: Motion and Topology Consistency Guided Learning for Mitral Valve Segmentationin 4D Ultrasound

Mitral regurgitation is one of the most prevalent cardiac disorders. Four-dimensional (4D) ultrasound has emerged as the primary imaging modality fo...

A comparative analysis of metamodels for 0D cardiovascular models, and pipeline for sensitivity analysis, parameter estimation, and uncertainty quantification.

Zero-dimensional (0D) cardiovascular models are reduced-order models aimed at studying the global dynamics of the whole circulation system or transpor...

Precision medicine applications in dilated cardiomyopathy: Advancing personalized care.

Dilated cardiomyopathy (DCM) is a prevalent cardiac disorder affecting 1 in 250-500 individuals, characterized by ventricular dilation and impaired sy...

An informed machine learning based environmental risk score for hypertension in European adults.

BACKGROUND: The exposome framework seeks to unravel the cumulated effects of environmental exposures on health. However, existing methods struggle wit...

MD-ViSCo: A Unified Model for Multi-Directional Vital Sign Waveform Conversion

Despite the remarkable progress of deep-learning methods generating a target vital sign waveform from a source vital sign waveform, most existing mo...

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data

Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalizat...

Vision transformer-based stratification of pre/diabetic and pre/hypertensive patients from retinal photographs for 3PM applications.

OBJECTIVE: Diabetes and hypertension pose significant health risks, especially when poorly managed. Retinal evaluation though fundus photography can p...

Deep Learning Algorithms to Predict Differential Renal Function <40% in Unilateral Hydronephrosis Based on Key Parameters of Urinary Tract Ultrasound.

OBJECTIVE: To identify the correlation between ultrasound findings and the incidence of differential renal function (DRF) <40%, we conducted an analys...

Predicting blood pressure without a cuff using a unique multi-modal wearable device and machine learning algorithm.

Blood pressure is a critical risk factor for cardiovascular diseases (CVDs), yet most adults do not monitor it frequently enough to prevent serious co...

Deep convolutional fuzzy neural networks with stork optimization on chronic cardiovascular disease monitoring for pervasive healthcare services.

Cardiovascular disease (CVD) is one of the severe disorders that requires effectual solutions. CVD mainly affects heart functionality in the human bod...

Intelligent Incident Hypertension Prediction in Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is a significant risk factor for hypertension, primarily due to intermittent hypoxia and sleep fragmentation. Predicti...