AIMC Topic: Cardiovascular Diseases

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An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks.

Sensors (Basel, Switzerland)
Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The s...

Systems biology in cardiovascular disease: a multiomics approach.

Nature reviews. Cardiology
Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for anal...

How wide is the application of genetic big data in biomedicine.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
In the era of big data, massive genetic data, as a new industry, has quickly swept almost all industries, especially the pharmaceutical industry. As countries around the world start to build their own gene banks, scientists study the data to explore ...

Nonparametric variable importance assessment using machine learning techniques.

Biometrics
In a regression setting, it is often of interest to quantify the importance of various features in predicting the response. Commonly, the variable importance measure used is determined by the regression technique employed. For this reason, practition...

Steps to use artificial intelligence in echocardiography.

Journal of echocardiography
Artificial intelligence (AI) has influenced every field of cardiovascular imaging in all phases from acquisition to reporting. Compared with computed tomography and magnetic resonance imaging, there is an issue of high observer variation in the inter...

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management.

Nature reviews. Cardiology
Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in p...

Risk stratification for mortality in cardiovascular disease survivors: A survival conditional inference tree analysis.

Nutrition, metabolism, and cardiovascular diseases : NMCD
BACKGROUND AND AIMS: Efficient analysis strategies for complex network with cardiovascular disease (CVD) risk stratification remain lacking. We sought to identify an optimized model to study CVD prognosis using survival conditional inference tree (SC...