AI Medical Compendium Topic:
Cardiovascular Diseases

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Using local lexicalized rules to identify heart disease risk factors in clinical notes.

Journal of biomedical informatics
Heart disease is the leading cause of death globally and a significant part of the human population lives with it. A number of risk factors have been recognized as contributing to the disease, including obesity, coronary artery disease (CAD), hyperte...

Non-redundant association rules between diseases and medications: an automated method for knowledge base construction.

BMC medical informatics and decision making
BACKGROUND: The widespread use of electronic health records (EHRs) has generated massive clinical data storage. Association rules mining is a feasible technique to convert this large amount of data into usable knowledge for clinical decision making, ...

A novel neural-inspired learning algorithm with application to clinical risk prediction.

Journal of biomedical informatics
Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for...

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...

[Artificial intelligence in cardiology: definition, types, glossary, algorithms used - opportunities, limitations, development barriers, and challenges].

Giornale italiano di cardiologia (2006)
Artificial intelligence (AI) is revolutionizing cardiology, offering new opportunities to improve diagnosis, therapy, and prevention of cardiovascular diseases. By analyzing large amounts of data and supporting clinical decisions, AI can simplify mod...

Expanding interpretability through complexity reduction in machine learning-based modelling of cardiovascular disease: A myocardial perfusion imaging PET/CT prognostic study.

European journal of clinical investigation
BACKGROUND: Machine learning-based analysis can be used in myocardial perfusion imaging data to improve risk stratification and the prediction of major adverse cardiovascular events for patients with suspected or established coronary artery disease. ...

The role of artificial intelligence in cardiovascular research: Fear less and live bolder.

European journal of clinical investigation
BACKGROUND: Artificial intelligence (AI) has captured the attention of everyone, including cardiovascular (CV) clinicians and scientists. Moving beyond philosophical debates, modern cardiology cannot overlook AI's growing influence but must actively ...

Deep learning for electrocardiogram interpretation: Bench to bedside.

European journal of clinical investigation
BACKGROUND: Recent advancements in deep learning (DL), a subset of artificial intelligence, have shown the potential to automate and improve disease recognition, phenotyping and prediction of disease onset and outcomes by analysing various sources of...

Applications, challenges and future directions of artificial intelligence in cardio-oncology.

European journal of clinical investigation
BACKGROUND: The management of cardiotoxicity related to cancer therapies has emerged as a significant clinical challenge, prompting the rapid growth of cardio-oncology. As cancer treatments become more complex, there is an increasing need to enhance ...

Machine learning in cardiovascular risk assessment: Towards a precision medicine approach.

European journal of clinical investigation
Cardiovascular diseases remain the leading cause of global morbidity and mortality. Validated risk scores are the basis of guideline-recommended care, but most scores lack the capacity to integrate complex and multidimensional data. Limitations inher...