一个基于变压器架构的COVID-19搜索引擎 (CO-SE)
1Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Healthcare analytics (New York, N.Y.)
|July 31, 2023
概括
一个新的COVID-19搜索引擎 (CO-SE) 帮助研究人员从广泛的科学文献中找到精确的信息. 该系统使用检索和阅读组件来回答有关SARS-CoV-2的复杂问题,改善对关键COVID-19研究结果的访问.
科学领域:
- 信息检索 信息检索
- 计算生物学 计算生物学
- 医疗信息学 医疗信息学
背景情况:
- 随着COVID-19研究的指数增长,医疗保健专业人员和科学家在获得及时和准确信息方面面临挑战.
- 现有的方法难以跟上SARS-CoV-2出版物的数量,阻碍了知识传播.
- 需要专门的工具来驾复杂的COVID-19科学文献.
研究的目的:
- 设计和开发COVID-19搜索引擎 (CO-SE),这是一个高效信息检索的算法系统.
- 使用户能够从大量的COVID-19出版物中找到相关文件并获得复杂问题的答案.
- 为从业者,前线工作者和研究人员提供专家特定的方法,以保持最新的SARS-CoV-2研究.
主要方法:
- 开发了一个由两个组件组成的系统:一个检索器和一个读器.
- 检索器使用TF-IDF矢量标识器从出版物库中识别相关文档.
- 阅读器使用基于变压器的模型,根据用户查询从检索的文档中提取精确的答案.
主要成果:
- CO-SE系统实现了精确匹配比率的得分71.45%.
- 该系统的语义答案相似度得分为78.55%.
- 拟议的方法优于现有的模型和基准数据集,表明强大的通用性.
结论:
- COVID-19搜索引擎 (CO-SE) 提供了一种有效的解决方案,用于导航广泛的COVID-19研究.
- 该系统在检索相关信息和回答复杂问题方面的表现超过了以前的基准.
- CO-SE提高了对关键科学知识的可访问性,支持研究人员和从业人员在抗击COVID-19的斗争中.
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