在社交媒体上,大语言建模启用了对心房的分析
Shyon Parsa1, Sulaiman Somani1, Albert J Rogers2
1Department of Internal Medicine Stanford University Hospital Stanford CA USA.
Journal of the American Heart Association
|December 17, 2025
概括
对社交媒体的人工智能分析揭示了患有心房动 (AF) 的患者经历. 关键主题包括治疗感知,监测设备,以及生活在AF的人面临的挑战.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 心房动 (AF) 是一种普遍存在的全球性心律失常,影响治疗决策.
- 患者的看法对于AF管理中有效的共享决策至关重要.
- 社交媒体分析提供了对外临床环境外AF的公众态度的见解.
研究的目的:
- 用人工智能对社交媒体数据分析公众对心房动 (AF) 的看法.
- 确定与AF检测和管理相关的关键主题和以患者为中心的挑战.
主要方法:
- 使用大语言建模和AI主题建模进行定性研究.
- 从2006年4月到2023年11月,分析了86,323次与AF相关的Reddit对话.
- 确定了65个不同的讨论主题,分为9个主题组.
主要成果:
- 确定的主题包括治疗经验,医疗保健提供者角色,社区支持和AF触发因素.
- 主要讨论的重点是用于AF监控的可穿戴设备.
- 关键主题揭示了对AF治疗的犹和诊断后以患者为中心的挑战.
结论:
- 人工智能支持的分析突出了关于AF检测和管理的重大公共话语.
- 社交媒体数据为患者对心血管健康的看法提供了宝贵的见解.
- 这些发现可以为以患者为中心的资源和未来的研究提供信息,以改善AF患者的支持.
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