AIMC Topic: Cardiovascular Diseases

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Semantic web-based ontology: a comprehensive framework for cardiovascular knowledge representation.

BMC cardiovascular disorders
In the healthcare industry, the Semantic Web offers to manage a huge amount of medical data which is machine-readable and machine-understandable as well. This domain incorporates ontologies, linked data, and semantic web technologies to promote healt...

Comprehensive interaction modeling with machine learning improves prediction of disease risk in the UK Biobank.

Nature communications
Understanding how risk factors interact to jointly influence disease risk can provide insights into disease development and improve risk prediction. Here we introduce survivalFM, a machine learning extension to the widely used Cox proportional hazard...

An academic evaluation of ChatGpt's ability and accuracy in creating patient education resources for rare cardiovascular diseases.

Scientific reports
Generative Pre-trained Transformer (ChatGPT) is a web-based artificial intelligence assistant with the potential to provide information, answer questions, and make recommendations on various topics. Rare cardiovascular diseases (rCVD) are among the h...

Wearable technology for cardiovascular disease management: A global bibliometric analysis with emerging insights into artificial intelligence integration.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Wearable technology has become increasingly essential in managing cardiovascular disease (CVD), offering innovative solutions for real-time monitoring and personalized care. Artificial intelligence (AI) is playing a growing ...

The association of life's essential 8 with prevalence of chronic respiratory diseases in adults: insights from NHANES 2007-2018.

BMC pulmonary medicine
OBJECTIVE: Chronic respiratory diseases (CRDs) and cardiovascular diseases (CVD) share common risk factors and frequently co-occur, leading to poorer outcomes. Life's Essential 8 (LE8), a novel metric for cardiovascular health, may provide insights i...

Efficient pretraining of ECG scalogram images using masked autoencoders for cardiovascular disease diagnosis.

Scientific reports
Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, emphasizing the need for accurate and early diagnosis. Electrocardiograms (ECG) provide a non-invasive means of diagnosing various cardiac conditions. However, traditional m...

Design and analysis of TwinCardio framework to detect and monitor cardiovascular diseases using digital twin and deep neural network.

Scientific reports
World Health Organization (WHO) estimates 17.9 million deaths globally every year due to Cardiovascular Disease or CVD, which includes an array of disorders of the heart and blood vessels, that includes coronary heart disease, cerebrovascular disease...

Artificial Intelligence-Enabled Point-of-Care Echocardiography: Bringing Precision Imaging to the Bedside.

Current atherosclerosis reports
PURPOSE OF REVIEW: The integration of artificial intelligence (AI) with point-of-care ultrasound (POCUS) is transforming cardiovascular diagnostics by enhancing image acquisition, interpretation, and workflow efficiency. These advancements hold promi...

Network-based machine learning reveals cardiometabolic multimorbidity patterns and modifiable lifestyle factors: a community-focused analysis of NHANES 2015-2018.

BMC public health
Cardiometabolic Multimorbidity (CMM) has emerged as one of the primary threats to human health globally due to its high incidence, disability, and mortality rates. Accurate identification of CMM patterns is crucial for CMM classification and health m...