Hypertension

Accelerated Patient-Specific Calibration via Differentiable Hemodynamics Simulations

One of the goals of personalized medicine is to tailor diagnostics to individual patients. Diagnostics are performed in practice by measuring quantities, called biomarkers, that indicate the existence and progress of a disease. In common cardiovascular diseases, such as hypertension, biomarkers that are closely related to the clinical representation of a patient can be predicted using computatio...

IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations

Capturing geometric and material information from images remains a fundamental challenge in computer vision and graphics. Traditional optimization-based methods often require hours of computational time to reconstruct geometry, material properties, and environmental lighting from dense multi-view inputs, while still struggling with inherent ambiguities between lighting and material. On the other...

Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads

The rapid adaptation of data driven AI models, such as deep learning inference, training, Vision Transformers (ViTs), and other HPC applications, dr...

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization

Vector quantization(VQ) is a hardware-friendly DNN compression method that can reduce the storage cost and weight-loading datawidth of hardware acce...

Determinants of developing cardiovascular disease risk with emphasis on type-2 diabetes and predictive modeling utilizing machine learning algorithms.

This research aims to enhance our comprehensive understanding of the influence of type-2 diabetes on the development of cardiovascular diseases (CVD) ...

[Study on predicting new onset heart failure events in patients with hypertrophic cardiomyopathy using machine learning algorithms based on clinical and magnetic resonance features].

To explore the value of predicting new-onset heart failure events in patients with hypertrophic cardiomyopathy (HCM) using clinical and cardiac magne...

Primary care research on hypertension: A bibliometric analysis using machine-learning.

Hypertension is one of the most important chronic diseases worldwide. Hypertension is a critical condition encountered frequently in daily life, formi...

Elucidating the cellular determinants of the end-systolic pressure-volume relationship of the heart via computational modelling

The left ventricular end-systolic pressure-volume relationship (ESPVr) is a key indicator of cardiac contractility. Despite its established importan...

TATAA: Programmable Mixed-Precision Transformer Acceleration with a Transformable Arithmetic Architecture

Modern transformer-based deep neural networks present unique technical challenges for effective acceleration in real-world applications. Apart from ...

Electromechanical Dynamics of the Heart: A Study of Cardiac Hysteresis During Physical Stress Test

Cardiovascular diseases are best diagnosed using multiple modalities that assess both the heart's electrical and mechanical functions. While effecti...

Regional End-Systolic Circumferential Strain Demonstrates Reduced Function in Remote Myocardium after Anterior STEMI

Anterior ST-segment elevation myocardial infarction (STEMI) is associated with severe adverse remodeling and increased mortality rates. In this stud...

Identification of fatty acid metabolism signature genes in patients with pulmonary arterial hypertension using WGCNA and machine learning.

OBJECTIVE: To investigate the signature genes of fatty acid metabolism and their association with immune cells in pulmonary arterial hypertension (PAH...

Bridging Gaps with Generative AI: Enhancing Hypertension Monitoring Through Patient and Provider Insights.

This study introduces a Generative Artificial Intelligence (GenAI) assistant designed to address key challenges in Remote Patient Monitoring (RPM) for...

EdgeLLM: A Highly Efficient CPU-FPGA Heterogeneous Edge Accelerator for Large Language Models

The rapid advancements in artificial intelligence (AI), particularly the Large Language Models (LLMs), have profoundly affected our daily work and c...

Deep Learning for identifying systolic complexes in SCG traces: a cross-dataset analysis.

The seismocardiographic signal is a promising alternative to the traditional ECG in the analysis of the cardiac activity. In particular, the systolic ...

Minimally invasive monitor of cardiac output based on the machine-learning analysis of the pulse contour of the peripheral arterial pressure.

In the hemodynamic management of anesthetized patients during surgical operation, minimally invasive and accurate cardiac output (CO) monitoring is st...

Machine Learning Approaches for Blood Pressure Classification from Photoplethysmogram: A Comparative Analysis.

The cuffless estimation of blood pressure (BP) has become a prominent area of research in recent years fueled by its potential clinical implications a...

An Open-Source Framework for Efficient Numerically-Tailored Computations

We present a versatile open-source framework designed to facilitate efficient, numerically-tailored Matrix-Matrix Multiplications (MMMs). The framew...

Dissecting unique and common variance across body and brain health indicators using age prediction.

Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current s...

Deep learning to assess right ventricular ejection fraction from two-dimensional echocardiograms in precapillary pulmonary hypertension.

BACKGROUND: Precapillary pulmonary hypertension (PH) is characterized by a sustained increase in right ventricular (RV) afterload, impairing systolic ...