In 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 status, they are infrequently used for several reasons, including modest performance, lack of evidence to support routine clinical use, and barriers to implementation. Artificial intelligence has the potential to enhance the performance of risk prediction models, but has its own limitations and remains unproved.
Wehbe Ramsey M, Khan Sadiya S, Shah Sanjiv J, Ahmad Faraz S
Artificial intelligence, Deep learning, Heart failure, Machine learning, Prognosis, Risk factors, Risk models, Risk scores