AIMC Topic: Cardiovascular Diseases

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Machine learning models of depression in middle-aged and older adults with cardiovascular metabolic diseases.

Journal of affective disorders
BACKGROUND: The incidence of cardiovascular metabolic diseases (CMD) is increasing, and depression in CMD patients significantly impacts prognosis. Therefore, this study aimed to develop and validate a predictive model for depression in CMD patients ...

[Artificial intelligence-enhanced ECG interpretation: a new era for electrocardiography?].

Giornale italiano di cardiologia (2006)
Artificial intelligence (AI) is redefining ECG interpretation, transforming it from a static diagnostic tool into a dynamic, predictive, and integrative instrument. Although widespread, traditional rule-based ECG analysis has limitations in accuracy ...

Deep learning framework for cardiorespiratory disease detection using smartphone IMU sensors.

Computers in biology and medicine
Respiratory and cardiovascular diseases represent a significant global health burden, underscoring the need for innovative, accessible, and cost-effective screening solutions. This study introduces a clinically grounded framework for the early detect...

Assessing the effect of perfluoroalkyl and polyfluoroalkyl substances on cardiovascular-kidney-metabolic syndrome: Insights from an interpretable machine learning model.

The Science of the total environment
Cardiovascular-kidney-metabolic syndrome (CKM) and its association with exposure to emerging pollutants, particularly perfluoroalkyl and polyfluoroalkyl substances (PFAS), present significant challenges for environmental public health and risk predic...

How to measure and model cardiovascular aging.

Cardiovascular research
Most acquired cardiovascular diseases are more common in older people, and the biological mechanisms and manifestations of aging provide insight into cardiovascular pathophysiology. Measuring aging within the cardiovascular system may help to better ...

The Impact of Social Determinants on Cardiovascular Mortality: A Zip Code-Level Analysis in Indiana.

Studies in health technology and informatics
Cardiovascular disease (CVD) is the leading cause of death globally and is expected to become the top global cause of death by 2030. Although the role of social determinants of health (SDoH) in CVD outcomes is well-established, integrating these fact...

AI Bias and Confounding Risk in Health Feature Engineering for Machine Learning Classification Task.

Studies in health technology and informatics
Recent advancements in machine learning bring unique opportunities in health fields but also pose considerable challenges. Due to stringent ethical considerations and resource constraints, health data can vary in scope, population coverage, and colle...

[Artificial intelligence for randomized controlled trials in cardiology: applications and future perspectives].

Giornale italiano di cardiologia (2006)
Integrating artificial intelligence (AI) into cardiovascular clinical trials is emerging as a key factor in streamlining patient selection, data collection, endpoint monitoring, and outcome analysis. On the one hand, machine learning and deep learnin...

Personalized medicine for cardiovascular diseases: how next generation epigenetic technologies can contribute?

Epigenomics
Advances in DNA methylation and artificial intelligence have led to new methods for assessing risk and diagnosing coronary heart disease (CHD), the leading cause of death. However, whether these technologies can also be harnessed to generate new phar...