Park Hyewon, Kim Siho, Kim Gaeun, Chang Seunghyeok, Shin Jae-Gook, Ahn Sangzin
Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Korea.
Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Korea.
Healthc Inform Res. 2025 Jul;31(3):263-273. doi: 10.4258/hir.2025.31.3.263. Epub 2025 Jul 31.
This study was conducted to investigate public perceptions and concerns surrounding tuberculosis (TB) treatment in Korea through an analysis of online queries about antitubercular medications. Additionally, it evaluated the effectiveness of large language models (LLMs) as analytical tools for processing unstructured healthcare data.
Using LLMs, this study analyzed 44,174 questions that mentioned TB from Naver Knowledge-iN (2002-2024). Questions referencing antitubercular medications were extracted and thematically categorized. Side effects were analyzed through parallel approaches examining general and medication-specific effects. Questions about infectivity and social implications were further analyzed using text embedding, dimensionality reduction, and clustering. The performance of LLMs was evaluated against human researchers and traditional methods.
Among questions mentioning specific medications (n = 919), rifampin (31.8%) and isoniazid (31.6%) were most frequently referenced. Of the 10,044 questions regarding antitubercular medication, management challenges represented the largest category (44.8%). Analysis of infectivity and social implications (n = 583) revealed previously unidentified concerns about blood donation and immigration eligibility. Employment-related concerns constituted the largest distinct subgroup (20.6%). Hepatotoxicity, dermatosis, and vomiting were the most frequently reported side effects. LLMs outperformed keyword matching in data processing and offered cost advantages over human analysis, with finetuning further reducing processing costs.
This study produced novel insights into public concerns regarding TB treatment and demonstrated the effectiveness of combining social media platform data with LLM-based analysis, providing a systematic framework for future healthcare research using unstructured public data and LLMs.
本研究旨在通过分析关于抗结核药物的在线查询，调查韩国公众对结核病（TB）治疗的看法和担忧。此外，还评估了大语言模型（LLMs）作为处理非结构化医疗数据的分析工具的有效性。
本研究使用大语言模型，分析了来自Naver Knowledge-iN（2002 - 2024年）中提到结核病的44,174个问题。提取了提及抗结核药物的问题并进行主题分类。通过检查一般和特定药物副作用的并行方法分析副作用。使用文本嵌入、降维和聚类进一步分析关于传染性和社会影响的问题。将大语言模型的性能与人类研究人员和传统方法进行了比较。
在提及特定药物的问题中（n = 919），利福平（31.8%）和异烟肼（31.6%）被提及的频率最高。在10,044个关于抗结核药物的问题中，管理挑战占最大类别（44.8%）。对传染性和社会影响的分析（n = 583）揭示了先前未被发现的关于献血和移民资格的担忧。与就业相关的担忧构成了最大的不同亚组（20.6%）。肝毒性、皮肤病和呕吐是最常报告的副作用。大语言模型在数据处理方面优于关键词匹配，并且与人工分析相比具有成本优势，微调进一步降低了处理成本。
本研究对公众对结核病治疗的担忧产生了新的见解，并证明了将社交媒体平台数据与基于大语言模型的分析相结合的有效性，为未来使用非结构化公共数据和大语言模型的医疗研究提供了一个系统框架。
