Large Language Modeling-Enabled Analysis of Atrial Fibrillation on Social Media.

Area of Science:

• Cardiology
• Artificial Intelligence
• Public Health

Background:

• Atrial fibrillation (AF) is a prevalent global arrhythmia impacting treatment decisions.
• Patient perceptions are crucial for effective shared decision-making in AF management.
• Social media analysis offers insights into public attitudes toward AF outside clinical settings.

Purpose of the Study:

• To analyze public perceptions of atrial fibrillation (AF) using artificial intelligence on social media data.
• To identify key themes and patient-centered challenges related to AF detection and management.

Main Methods:

• Qualitative study utilizing large language modeling and AI topic modeling.
• Analysis of 86,323 AF-related Reddit conversations from April 2006 to November 2023.
• Identification of 65 distinct discussion topics organized into 9 thematic groups.

Main Results:

• Identified themes include treatment experiences, healthcare provider roles, community support, and AF triggers.
• Prominent discussions centered on wearable devices for AF monitoring.
• Key themes revealed hesitancy towards AF treatments and patient-centered challenges post-diagnosis.

Conclusions:

• AI-enabled analysis highlights significant public discourse on AF detection and management.
• Social media data provides valuable insights into patient perspectives on cardiovascular health.
• Findings can inform patient-centered resources and future research for improved AF patient support.