A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.

Abstract excerpt

BACKGROUND: Heart failure is not only a prevalent disease with a high mortality rate, but also generates high costs for healthcare systems. By training artificial intelligence (AI) models on medical data, it is possible to predict changes in health status that may lead to hospital readmissions or death. Such predictions enable better patient care and a proactive response to deterioration. METHODS: We conducted a...

Topics

Share this publication in a Topic to start or enrich a Post.

• Share to Electronic Health Records
• Share to Heart Failure