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大型语言模型在社交媒体上揭示了月经经验和需求

Charlotte Tumescheit^1,2, Davinny Sou^3,4, Marcia Nißen^3,4

¹Institute for Implementation Science in Health Care, University of Zurich, Switzerland.

Studies in health technology and informatics

|August 8, 2025

概括

这项研究使用社交媒体数据和人工智能来了解月经健康需求. 研究结果强调了情感支持和社区对于那些面临症状驳回的个体的重要性.

科学领域:

医学研究医学研究
社交媒体分析
医疗保健中的人工智能

背景情况:

医学界存在很大的性别知识差距,特别是在月经和子宫内膜异位症等疾病方面.
这种差距导致许多个人的诊断延迟和不理想的护理.
患者经常转向像TikTok和YouTube这样的在线平台,以获取信息和支持,因为症状被驳回.

研究的目的:

利用社交媒体数据来识别反映经历和需求的关键话题.
弥合月经健康研究现有的知识差距.
了解周期不规则的代表性不足的个体的独特需求.

主要方法:

开发了一个新的管道来分析来自社交媒体平台的视频评论.
用BERTopic和Llama 3.1模型进行主题建模和分析.
该研究的重点是确定与月经和相关健康状况有关的主题.

主要成果:

确定的主要话题包括需要情感支持,教育指导和社区验证.
这些发现与以前关于患者体验的研究一致.
关于代表性不足的个体,包括周期不规则的个体的月经经历,揭示了独特的见解.

结论:

社交媒体平台提供了关于月经健康经验的宝贵见解.
大型语言模型可以增强包容性的月经健康研究.
解决性别知识差距需要理解和验证多样化的患者经验,特别是被忽视的人群.

关键词:

个人的需求和经验.在法学士 (LLM) 课程中.拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛拉玛月经健康月经健康经期月经月经.在NLP中,我们使用了NLP.社交媒体社交媒体主题建模主题建模女性的健康妇女的健康

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