Accuracy and consistency of online large language model-based artificial intelligence chat platforms in answering patients' questions about heart failure.

Background: Heart failure (HF) is a prevalent condition associated with significant morbidity. Patients may have questions that they feel embarrassed to ask or will face delays awaiting responses from their healthcare providers which may impact their health behavior. We aimed to investigate the potential of large language model (LLM) based artificial intelligence (AI) chat platforms in complementing the delivery of patient-centered care.

Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.

Background: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.

Objective: To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score.

Bee Venom and Its Two Main Components-Melittin and Phospholipase A2-As Promising Antiviral Drug Candidates.

Viruses are known to infect most types of organisms. In humans, they can cause several diseases that range from mild to severe. Although many antiviral therapies have been developed, viral infections continue to be a leading cause of morbidity and mortality worldwide.