AIMC Topic: Cardiovascular Diseases

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Fusion of multi-scale feature extraction and adaptive multi-channel graph neural network for 12-lead ECG classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The 12-lead electrocardiography (ECG) is a widely used diagnostic method in clinical practice for cardiovascular diseases. The potential correlation between interlead signals is an important reference for clinical diagnosis ...

OptiStack classifier: optimized stacking framework with ensemble feature engineering for enhanced cardiovascular risk prediction.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]
BACKGROUND: Cardiovascular diseases (CVD) are a leading cause of morbidity and mortality globally, highlighting the urgent need for accurate risk prediction to improve early intervention and management. Traditional models have difficulty capturing th...

Deep convolutional fuzzy neural networks with stork optimization on chronic cardiovascular disease monitoring for pervasive healthcare services.

Scientific reports
Cardiovascular disease (CVD) is one of the severe disorders that requires effectual solutions. CVD mainly affects heart functionality in the human body. The impacts of heart disorders are hazardous, which primarily spread from arrhythmia and higher h...

Application of machine learning algorithms in osteoporosis analysis based on cardiovascular health assessed by life's essential 8: a cross-sectional study.

Journal of health, population, and nutrition
BACKGROUND: Life's Essential 8 (LE8) for assessing cardiovascular health (CVH) has been demonstrated to be inversely associated with osteoporosis (OP). This study aims to create a machine learning (ML) model to assess the clinical association value o...

Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal study.

BMC public health
BACKGROUND: The incidence of cardiovascular metabolic diseases (CMD) continues to rise among middle-aged and elderly populations, affecting not only physical health but also significantly increasing the risk of depression. This study aims to construc...

Predicting cardiovascular risk with hybrid ensemble learning and explainable AI.

Scientific reports
Cardiovascular diseases (CVDs) are still one of the leading causes of death globally, underscoring the importance of early and right risk prediction for effective preventive measures and therapeutic approaches. This study proposes an innovative hybri...

The Benefits and Challenges of Digitally-Enabled Cardiology.

British journal of hospital medicine (London, England : 2005)
Digital health technologies, including artificial intelligence, offer immense potential to revolutionise cardiology by improving patient care, enhancing efficiency, and increasing access to specialised services. Benefits may include precision medicin...

Artificial intelligence to improve cardiovascular population health.

European heart journal
With the advent of artificial intelligence (AI), novel opportunities arise to revolutionize healthcare delivery and improve population health. This review provides a state-of-the-art overview of recent advancements in AI technologies and their applic...

Breast Arterial Calcifications on Mammography: A Review of the Literature.

Journal of breast imaging
Identifying systemic disease with medical imaging studies may improve population health outcomes. Although the pathogenesis of peripheral arterial calcification and coronary artery calcification differ, breast arterial calcification (BAC) on mammogra...