Understanding the language of the heart: The promise of natural language processing to diagnose heart failure with preserved ejection fraction.

This article discusses the challenges in diagnosing heart failure with preserved ejection fraction (HFpEF) and the potential of artificial intelligence (AI) and natural language processing (NLP) to improve diagnosis. HFpEF is difficult to diagnose and often underdiagnosed, leading to missed opportunities for treatment and management. The study by Wu et al. applied NLP to a database of patients with a clinical diagnosis of heart failure and found a significant disparity in the diagnosis rates of HFpEF. NLP can aid clinicians in identifying high-risk patients and prompt further investigation for accurate diagnosis and personalized treatment. The incorporation of AI and NLP into HFpEF detection shows promise for enhancing diagnosis, monitoring disease progression, and tailoring treatment regimens. However, further research, validation, and ethical considerations are needed for the full integration of AI and NLP into clinical practice. [Extracted from the article]