AIMC Topic: Cardiovascular Diseases

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Using Machine Learning to Evaluate the Role of Microinflammation in Cardiovascular Events in Patients With Chronic Kidney Disease.

Frontiers in immunology
BACKGROUND: Lipid metabolism disorder, as one major complication in patients with chronic kidney disease (CKD), is tied to an increased risk for cardiovascular disease (CVD). Traditional lipid-lowering statins have been found to have limited benefit ...

Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units.

Computational and mathematical methods in medicine
BACKGROUND: A survival prediction model based on deep learning has higher accuracy than the CPH model in predicting the survival of CCU patients, and it also has a better discrimination ability. We collected information on patients with various disea...

Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study.

The Lancet. Digital health
BACKGROUND: Echocardiography is the diagnostic modality for assessing cardiac systolic and diastolic function to diagnose and manage heart failure. However, manual interpretation of echocardiograms can be time consuming and subject to human error. Th...

A Primer on the Present State and Future Prospects for Machine Learning and Artificial Intelligence Applications in Cardiology.

The Canadian journal of cardiology
The artificial intelligence (AI) revolution is well underway, including in the medical field, and has dramatically transformed our lives. An understanding of the basics of AI applications, their development, and challenges to their clinical implement...

The Role of Machine Learning in Cardiovascular Pathology.

The Canadian journal of cardiology
Machine learning has seen slow but steady uptake in diagnostic pathology over the past decade to assess digital whole-slide images. Machine learning tools have incredible potential to standardise, and likely even improve, histopathologic diagnoses, b...

A weighted patient network-based framework for predicting chronic diseases using graph neural networks.

Scientific reports
Chronic disease prediction is a critical task in healthcare. Existing studies fulfil this requirement by employing machine learning techniques based on patient features, but they suffer from high dimensional data problems and a high level of bias. We...

Prospects for cardiovascular medicine using artificial intelligence.

Journal of cardiology
As the importance of artificial intelligence (AI) in the clinical setting increases, the need for clinicians to understand AI is also increasing. This review focuses on the fundamental principles of AI and the current state of cardiovascular AI. Vari...

Artificial Intelligence in Cardiovascular Imaging: "Unexplainable" Legal and Ethical Challenges?

The Canadian journal of cardiology
Nowhere is the influence of artificial intelligence (AI) likely to be more profoundly felt than in health care, from patient triage and diagnosis to surgery and follow-up. Over the medium-term, these effects will be more acute in the cardiovascular i...

Machine learning predictive models of LDL-C in the population of eastern India and its comparison with directly measured and calculated LDL-C.

Annals of clinical biochemistry
BACKGROUND: LDL-C is a strong risk factor for cardiovascular disorders. The formulas used to calculate LDL-C showed varying performance in different populations. Machine learning models can study complex interactions between the variables and can be ...

The Evolving Role of Artificial Intelligence in Cardiac Image Analysis.

The Canadian journal of cardiology
Research in artificial intelligence (AI) has progressed over the past decade. The field of cardiac imaging has seen significant developments using newly developed deep learning methods for automated image analysis and AI tools for disease detection a...