AIMC Topic: Cardiovascular Diseases

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Sleep heart rate variability assists the automatic prediction of long-term cardiovascular outcomes.

Sleep medicine
OBJECTIVE: We aimed to investigate the association between sleep HRV and long-term cardiovascular disease (CVD) outcomes, and further explore whether HRV features can assist the automatic CVD prediction.

Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic Populations.

Journal of medical systems
Few studies have addressed the predictive value of arterial stiffness determined by pulse wave velocity (PWV) in a high-risk population with no prevalent cardiovascular disease and with obesity, hypertension, hyperglycemia, and preserved kidney funct...

Fast fully automatic heart fat segmentation in computed tomography datasets.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Heart diseases affect a large part of the world's population. Studies have shown that these diseases are related to cardiac fat. Various medical diagnostic aid systems are developed to reduce these diseases. In this context, this paper presents a new...

DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model.

International journal of molecular sciences
MicroRNAs (miRNAs) are a highly abundant collection of functional non-coding RNAs involved in cellular regulation and various complex human diseases. Although a large number of miRNAs have been identified, most of their physiological functions remain...

Machine learning with sparse nutrition data to improve cardiovascular mortality risk prediction in the USA using nationally randomly sampled data.

BMJ open
OBJECTIVES: We aimed to test whether or not adding (1) nutrition predictor variables and/or (2) using machine learning models improves cardiovascular death prediction versus standard Cox models without nutrition predictor variables.

Artificial Intelligence and Machine Learning in Cardiovascular Health Care.

The Annals of thoracic surgery
BACKGROUND: This review article provides an overview of artificial intelligence (AI) and machine learning (ML) as it relates to cardiovascular health care.

Analysis of nonstandardized stress echocardiography sequences using multiview dimensionality reduction.

Medical image analysis
Alternative stress echocardiography protocols such as handgrip exercise are potentially more favorable towards large-scale screening scenarios than those currently adopted in clinical practice. However, these are still underexplored because the maxim...

A data-driven approach to predicting diabetes and cardiovascular disease with machine learning.

BMC medical informatics and decision making
BACKGROUND: Diabetes and cardiovascular disease are two of the main causes of death in the United States. Identifying and predicting these diseases in patients is the first step towards stopping their progression. We evaluate the capabilities of mach...

Predicting atrial fibrillation in primary care using machine learning.

PloS one
BACKGROUND: Atrial fibrillation (AF) is the most common sustained heart arrhythmia. However, as many cases are asymptomatic, a large proportion of patients remain undiagnosed until serious complications arise. Efficient, cost-effective detection of t...

Deep Learning for Quantitative Cardiac MRI.

AJR. American journal of roentgenology
The recent advancement of deep learning techniques has profoundly impacted research on quantitative cardiac MRI analysis. The purpose of this article is to introduce the concept of deep learning, review its current applications on quantitative cardi...