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Contemporary attitudes and beliefs on coronary artery calcium from social media using artificial intelligence.


ABSTRACT: Coronary artery calcium (CAC) is a powerful tool to refine atherosclerotic cardiovascular disease (ASCVD) risk assessment. Despite its growing interest, contemporary public attitudes around CAC are not well-described in literature and have important implications for shared decision-making around cardiovascular prevention. We used an artificial intelligence (AI) pipeline consisting of a semi-supervised natural language processing model and unsupervised machine learning techniques to analyze 5,606 CAC-related discussions on Reddit. A total of 91 discussion topics were identified and were classified into 14 overarching thematic groups. These included the strong impact of CAC on therapeutic decision-making, ongoing non-evidence-based use of CAC testing, and the patient perceived downsides of CAC testing (e.g., radiation risk). Sentiment analysis also revealed that most discussions had a neutral (49.5%) or negative (48.4%) sentiment. The results of this study demonstrate the potential of an AI-based approach to analyze large, publicly available social media data to generate insights into public perceptions about CAC, which may help guide strategies to improve shared decision-making around ASCVD management and public health interventions.

SUBMITTER: Somani S 

PROVIDER: S-EPMC10981728 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Contemporary attitudes and beliefs on coronary artery calcium from social media using artificial intelligence.

Somani Sulaiman S   Balla Sujana S   Peng Allison W AW   Dudum Ramzi R   Jain Sneha S   Nasir Khurram K   Maron David J DJ   Hernandez-Boussard Tina T   Rodriguez Fatima F  

NPJ digital medicine 20240330 1


Coronary artery calcium (CAC) is a powerful tool to refine atherosclerotic cardiovascular disease (ASCVD) risk assessment. Despite its growing interest, contemporary public attitudes around CAC are not well-described in literature and have important implications for shared decision-making around cardiovascular prevention. We used an artificial intelligence (AI) pipeline consisting of a semi-supervised natural language processing model and unsupervised machine learning techniques to analyze 5,606  ...[more]

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