一项从生物医学文献中发现大规模疾病因果关系的研究
Shirui Yu1,2, Peng Dong3, Junlian Li3
1National Science Library (Chengdu), Chinese Academy of Sciences, Chengdu, 610041, China.
BMC medical informatics and decision making
|March 19, 2025
概括
这项研究通过开发语义预言词汇来增强SemRep工具,以准确地提取疾病因果关系. 这有助于提高生物医学知识的发现和疾病发病的理解.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 医学自然语言处理 医学自然语言处理
背景情况:
- 生物医学语义关系提取对于知识发现和AI应用至关重要.
- 识别疾病的因果关系有助于理解病变发生和改善治疗方法.
- 像SemRep这样的现有工具需要优化,以便精确地提取疾病因果关系.
研究的目的:
- 优化SemRep工具,以提高提取疾病因果关系的准确性.
- 为疾病因果关系开发精确的语义预言词汇.
- 支持从生物医学文献中自动提取疾病因果关系知识.
主要方法:
- 使用特征词和SemMedDB数据构建了一种疾病因果关系语义预言词汇.
- 通过定量比较过和评估线索词.
- 通过使用36个语义预言来提取疾病因果关系对,准确度>80%.
- 对提取的疾病因果关系三重 (单向,双向,初级,罕见) 进行了知识发现.
主要成果:
- 开发了50个文本预言的词汇,准确度>40%.
- 利用36个预言来提取,产生了259,434个疾病因果关系对.
- 确定了176,010个单向和83,424个双向的疾病因果关系三倍.
- 发现的初级和罕见疾病因果关系类型.
结论:
- 提出的方法显著提高了SemRep的疾病因果关系提取的准确性和全面性.
- 从大规模的生物医学文献中自动提取疾病因果关系.
- 允许使用量化预言词汇提取定制和灵活的疾病因果关系.
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