韩国结核病治疗的公众看法和障碍:对2002年至2024年的Naver Knowledge-iN数据进行了基于语言模型的大规模分析
Hyewon Park1, Siho Kim1, Gaeun Kim1
1Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Korea.
Healthcare informatics research
|August 21, 2025
概括
大型语言模型 (LLM) 分析了网上结核病 (TB) 治疗问题,揭示了公众对药物副作用和社会影响的担忧. 在分析非结构化医疗数据方面,LLM 已被证明是有效且具有成本效益的.
科学领域:
- 公共卫生
- 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 公众对结核病 (TB) 治疗的了解和关注对于有效的疾病管理至关重要.
- 分析非结构化在线数据可以了解公众对医疗保健的看法.
- 大型语言模型 (LLM) 是一种处理大量文本数据的新方法.
研究的目的:
- 通过在线查询调查公众对韩国结核病 (TB) 治疗的看法和担忧.
- 评估LLM在分析与结核病药物相关的非结构化医疗数据中的有效性.
主要方法:
- 通过使用LLM,对来自Naver Knowledge-iN (2002-2024) 的44174个TB相关问题进行分析.
- 针对抗结核药物,副作用,传染性和社会影响的问题进行主题分类.
- 与人类研究人员和传统方法相比,LLM的绩效评估.
主要成果:
- 里法和异酸是最常见的药物.
- 管理方面的挑战是最大的关注点 (44.8%).
- 关于献血和移民资格的新问题;就业是一个重要的社会问题 (20.6%).
- 肝毒性,皮肤病和吐是常见的副作用.
- 与传统方法相比,LLM表现出优越的数据处理,成本效益和效率.
结论:
- 基于LLM的在线数据分析为公众对结核病治疗的担忧提供了新的见解.
- 这种方法为未来的医疗保健研究提供了系统的框架,使用的是公开的非结构化数据.
- 这种方法是分析复杂的医疗信息和了解公众看法的有效工具.
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