Clinical Report: Correction on Language Models for Heart Failure Detection

Overview

This report addresses a correction made in the acknowledgments of a study on optimizing language models for detecting symptoms of heart failure with preserved ejection fraction (HFpEF) in Spanish electronic health records. The correction pertains to the resubmission of the corrected article to various repositories.

Background

Heart failure with preserved ejection fraction (HFpEF) is a significant clinical challenge, characterized by symptoms of heart failure despite a normal left ventricular ejection fraction. Accurate detection and management of HFpEF are critical for improving patient outcomes. The integration of advanced language models into electronic health records (EHRs) presents an innovative approach to enhance symptom detection and phenotyping in diverse populations.

Data Highlights

No numerical or trial data is presented in this correction notice.

Key Findings

• The correction was made to the acknowledgments section of the original study.
• The corrected article has been resubmitted to PubMed, PubMed Central, and other repositories.
• Language models can improve symptom detection in HFpEF when applied to EHR data.
• Accurate phenotyping is essential for effective management of HFpEF.
• Related studies highlight the potential of AI in enhancing EHR-based clinical assessments.

Clinical Implications

Healthcare professionals should be aware of the ongoing developments in AI applications for symptom detection in heart failure. The use of language models may facilitate better identification and management of HFpEF, leading to improved patient care.

Conclusion

The correction in the study underscores the importance of maintaining accuracy in clinical research publications, particularly in the context of evolving methodologies in heart failure detection.

Related Resources & Content

1. JMIR Publications, 2023 -- Correction: Optimization of Language Models for Detecting Symptoms of Heart Failure With Preserved Ejection Fraction in Spanish Electronic Health Records: A Comparative Study
2. aace endocrine ai — Selective LLM use may improve electronic health record phenotyping accuracy 
3. npj Digital Medicine — Enhanced Transferability of Predictions from Electronic Health Records Across Different Countries and Coding Frameworks Using Large Language Models
4. npj Digital Medicine — Diagnosis of cardiac conditions from 12-lead electrocardiogram through natural language supervision
5. Clinical Research in Cardiology — The Often Overlooked Incidence of Heart Failure Among Hospitalized Patients: An Analysis of ICD Codes Versus Discharge Summaries
6. Focus on Heart Failure | HFpEF: Where We Stand in 2025 - American College of Cardiology
7. The role of SGLT 2 inhibitors in heart failure with preserved ejection fraction (HFpEF): a systematic review and meta-analysis of randomized controlled trials
8. 2025 ACC Scientific Statement on the Management of Obesity in Adults With Heart Failure: A Report of the American College of Cardiology | JACC