跨多种关系类型的大规模生物医学关系提取:关于COVID-19的模型开发和可用性研究
Zeyu Zhang1,2, Meng Fang3, Rebecca Wu4
1Research Center for Translational Medicine, Shanghai East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.
Journal of medical Internet research
|August 26, 2023
概括
生物医学关系提取 (RE) 模型针对不同的关系类型进行了优化,改善了知识发现和临床应用. 增强的RE工具和模型,包括COVID-19关系图,现在已经公开供研究人员使用.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 计算生物学 计算生物学
背景情况:
- 生物医学关系提取 (RE) 对于系统研究,知识挖掘 (例如,知识图) 和翻译应用 (例如,临床诊断,精准医学) 至关重要.
- 当前的生物医疗 RE 面临着由于实体关系的复杂性和多样性而带来的挑战,而全面的系统尚未完善建立.
研究的目的:
- 调查和增强跨多种关系类型的大规模生物医学关系提取 (RE).
- 进行实用应用场景的可用性研究,以优化生物医学文本挖掘.
主要方法:
- 构建了125种关系类型和不同实体语义级别的数据集,以评估语义信息对RE性能的影响.
- 在不同的模型架构和特定领域模型上进行了性能分析,提出了持续预培训策略.
- 将RE应用于COVID-19体,评估在临床环境中的生物解释性和可用性.
主要成果:
- 通过详细的语义类型信息来最大限度地提高RE性能.
- 通过持续预训练,PubMedBERT在单个模型中获得了最高的F1得分0.8998.
- 对COVID-19数据的可用性研究证明了RE的可解释性和实用性,导致关系图数据库揭示了药物路径.
结论:
- 本研究为各种生物医学关系类型提供了RE的全面分析和优化,为文本挖掘提供了改进的模型和工具.
- 开发的RE模型和集成工具 (Docker) 是公开的,支持生物医学文本挖掘社区和临床研究人员.
- 可用性研究验证了大规模RE在促进新生物医学研究和发现方面的潜力.
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