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Cardiovascular Diseases

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Deep neural network-estimated electrocardiographic age as a mortality predictor.

Nature communications
The electrocardiogram (ECG) is the most commonly used exam for the evaluation of cardiovascular diseases. Here we propose that the age predicted by artificial intelligence (AI) from the raw ECG (ECG-age) can be a measure of cardiovascular health. A d...

Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran.

Scientific reports
This study was aimed to investigate the air pollutants impact on heart patient's hospital admission rates in Yazd for the first time. Modeling was done by time series, multivariate linear regression, and artificial neural network (ANN). During 5 year...

Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis.

Cardiovascular ultrasound
Ultrasound is one of the most important examinations for clinical diagnosis of cardiovascular diseases. The speed of image movements driven by the frequency of the beating heart is faster than that of other organs. This particularity of echocardiogra...

Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study.

Scientific reports
Given the rapid increase in the incidence of cardiometabolic conditions, there is an urgent need for better approaches to prevent as many cases as possible and move from a one-size-fits-all approach to a precision cardiometabolic prevention strategy ...

Natural language processing for the assessment of cardiovascular disease comorbidities: The cardio-Canary comorbidity project.

Clinical cardiology
OBJECTIVE: Accurate ascertainment of comorbidities is paramount in clinical research. While manual adjudication is labor-intensive and expensive, the adoption of electronic health records enables computational analysis of free-text documentation usin...

Machine-Learning-Derived Model for the Stratification of Cardiovascular risk in Patients with Ischemic Stroke.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
UNLABELLED: Background Stratification of cardiovascular risk in patients with ischemic stroke is important as it may inform management strategies. We aimed to develop a machine-learning-derived prognostic model for the prediction of cardiovascular ri...

The Role of Artificial Intelligence and Machine Learning in Clinical Cardiac Electrophysiology.

The Canadian journal of cardiology
In recent years, numerous applications for artificial intelligence (AI) in cardiology have been found, due in part to large digitized data sets and the evolution of high-performance computing. In the discipline of cardiac electrophysiology (EP), a nu...

Platform for Healthcare Promotion and Cardiovascular Disease Prevention.

IEEE journal of biomedical and health informatics
This article presents the hardware-software design and implementation of an open, integrated, and scalable healthcare platform oriented to multiple point-care scenarios for healthcare promotion and cardiovascular disease prevention. The platform has ...

A CNN model embedded with local feature knowledge and its application to time-varying signal classification.

Neural networks : the official journal of the International Neural Network Society
A novel convolutional neural network is proposed for local prior feature embedding and imbalanced dataset modeling for multi-channel time-varying signal classification. This model consists of a single-channel signal feature parallel extraction unit, ...