在生物医学文章中对传统配方与疾病的关系进行了注释
Sangjun Yea1, Ho Jang2,3, Soyoung Kim2,3
1Korean medicine data division, Korea Institute of Oriental Medicine, Daejeon, 34054, Republic of Korea. tomita@kiom.re.kr.
Scientific data
|January 8, 2025
概括
本研究介绍了传统配方与疾病关系 (TFDR) 库,这是NLP的新数据集. 它有助于从生物医学文献中提取有关传统配方和疾病的知识.
科学领域:
- 生物医学信息学 生物医学信息学
- 传统医学的传统医学.
- 自然语言处理自然语言处理.
背景情况:
- 传统配方 (TF) 正在获得作为替代医学的全球认可.
- 从文献中提取有关TF-疾病关系的知识是手工挑战.
- 自然语言处理 (NLP) 为自动化知识提取提供了潜力.
研究的目的:
- 为了引入传统配方与疾病关系 (TFDR) 库.
- 通过NLP促进TF疾病关系的自动提取.
- 解决传统医学研究中缺乏高质量的注释数据的问题.
主要方法:
- 从740个PubMed摘要中开发了一个手动注释的语料库 (TFDR).
- 包括提及传统配方 (TF) 和疾病.
- 在744个关键句子内注释了1,109个TF-疾病关系.
主要成果:
- 在TFDR的语料库中,有6211个TF提及和7166个疾病提及.
- 成功捕获了TF与疾病之间的1109种关系.
- 为传统医学中的NLP应用提供了宝贵的资源.
结论:
- TFDR库是推动传统医学NLP发展的关键资源.
- 能够实现TF-疾病相互作用的自动知识发现.
- 支持将传统医学见解融入生物医学科学.
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