FAIR数据库:促进对公共卫生研究文献的获取
Zhixue Zhao1, James Thomas2, Gregory Kell3
1Department of Computer Science, University of Sheffield, Sheffield S10 2TN, United Kingdom.
JAMIA open
|December 16, 2024
概括
我们使用自然语言处理创建了公共卫生研究的活数据库,以改善对健康不平等信息的获取. 该工具有助于研究人员和政策制定者更有效地识别相关研究.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 信息科学 信息科学 信息科学
背景情况:
- 获得公共卫生研究对于明智的决策和识别知识差距至关重要.
- 公共卫生干预是复杂的,在不同的环境中影响健康不平等.
- 现有的文献搜索方法在识别相关的,以不平等为重点的研究方面可能具有挑战性.
研究的目的:
- 为了发展一个
- 活着活着活着活着
- 公共卫生研究文献的数据库.
- 通过自然语言处理 (NLP) 工具,促进对健康不平等信息的获取.
- 为决策者和研究人员改善相关公共卫生研究的识别.
主要方法:
- 利用NLP工具开发研究设计和PROGRESS-Plus类别的自动分类器.
- 经过培训的分类人员使用现有的MEDLINE标签和注释的系统审查.
- 集成分类器输出到FAIR数据库中,以加强文献访问.
主要成果:
- 研究类型分类器实现了高性能 (平均精度为0.803,回忆为0.930).
- 进步-加分类提出了挑战 (平均精度为0.608,回忆为0.534).
- FAIR数据库有效地使用这些分类来帮助获取与不平等相关的文献.
结论:
- FAIR数据库提供了一个可行的解决方案,为公众提供可访问的,定期更新的公共卫生不平等资源.
- 这表明NLP在自动化公共卫生方面的证据合成方面的潜力.
- 该数据库是免费可用的,促进了对关键研究的更广泛访问.
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