AI Article Synopsis
- - Coronary artery calcium (CAC) testing is important for assessing the risk of atherosclerotic cardiovascular disease (ASCVD), but public perception of CAC and its implications for heart health decision-making are not well understood.
- - Researchers utilized an AI model to analyze 5,606 discussions on Reddit about CAC, identifying 91 topics categorized into 14 main themes, including the influence of CAC on treatment choices and concerns over testing risks.
- - Sentiment analysis of these discussions showed that nearly half expressed neutral or negative feelings towards CAC testing, highlighting a need for better communication and education to improve public understanding and shared decision-making in cardiovascular health.
Article 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10981728 | PMC |
| http://dx.doi.org/10.1038/s41746-024-01077-w | DOI Listing |
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