AI Medical Compendium Journal:
Heart failure clinics

Showing 1 to 9 of 9 articles

Emerging Roles for Artificial Intelligence in Heart Failure Imaging.

Heart failure clinics
Artificial intelligence (AI) applications are expanding in cardiac imaging. AI research has shown promise in workflow optimization, disease diagnosis, and integration of clinical and imaging data to predict patient outcomes. The diagnostic and progno...

The Emerging Role of Artificial Intelligence in Valvular Heart Disease.

Heart failure clinics
Valvular heart disease (VHD) is a morbid condition in which timely identification and evidence-based treatments can lead to improved outcomes. Artificial intelligence broadly refers to the ability for computers to perform tasks and problem solve like...

Using Artificial Intelligence to Better Predict and Develop Biomarkers.

Heart failure clinics
Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, tr...

Utilizing Conversational Artificial Intelligence, Voice, and Phonocardiography Analytics in Heart Failure Care.

Heart failure clinics
Conversational artificial intelligence involves the ability of computers, voice-enabled devices to interact intelligently with the user through voice. This can be leveraged in heart failure care delivery, benefiting the patients, providers, and payer...

Artificial Intelligence and Mechanical Circulatory Support.

Heart failure clinics
Advances in machine learning algorithms and computing power have fueled a rapid increase in artificial intelligence research in health care, including mechanical circulatory support. In this review, we highlight the needs for artificial intelligence ...

Machine Learning in Cardiovascular Imaging.

Heart failure clinics
The number of cardiovascular imaging studies is growing exponentially, and so is the demand to improve the efficacy of the imaging workflow. Over the past decade, studies have demonstrated that machine learning (ML) holds promise to revolutionize car...

Identification of Patients with Heart Failure in Large Datasets.

Heart failure clinics
Large registries, administrative data, and the electronic health record (EHR) offer opportunities to identify patients with heart failure, which can be used for research purposes, process improvement, and optimal care delivery. Identification of case...

Predicting High-Risk Patients and High-Risk Outcomes in Heart Failure.

Heart failure clinics
Identifying patients with heart failure at high risk for poor outcomes is important for patient care, resource allocation, and process improvement. Although numerous risk models exist to predict mortality, hospitalization, and patient-reported health...

Phenomapping Heart Failure with Preserved Ejection Fraction Using Machine Learning Cluster Analysis: Prognostic and Therapeutic Implications.

Heart failure clinics
Heart failure with preserved ejection fraction (HFpEF) is characterized by a high rate of hospitalization and mortality (up to 84% at 5 years), which are similar to those observed for heart failure with reduced ejection fraction (HFrEF). These epidem...