了解COVID-19:一个基于知识的系统,从在线医疗存储库中提取有针对性的COVID-19信息
Muzzamil Aziz1, Ioana Popa2,3, Amjad Zia2
1Future Networks, eScience Group, Gesellschaft für Wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG), 37077 Göttingen, Germany.
Biomolecules
|November 27, 2024
概括
了解COVID-19是一个新的系统,可以帮助研究人员更快地找到COVID-19信息. 它使用人工智能从医学文献中提取和组织数据,提高搜索准确性和效率.
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
- 医学数据科学 医学数据科学
背景情况:
- 获取和合成大量的生物医学文献,特别是关于COVID-19等快速发展的主题,为研究人员带来了重大挑战.
- 现有的搜索和数据挖掘工具往往缺乏足够的复杂性来有效地从各种在线存储库中提取和结构化有针对性的信息.
- 需要先进的系统来导航和分析越来越多的科学出版物对于加速研究和发现至关重要.
研究的目的:
- 介绍KnowVID-19,一个基于知识的新系统,旨在增强从在线医学文献中提取和分析信息.
- 为医学研究人员和科学家使用开源机器学习工具简化数据提取和挖掘过程.
- 通过先进的文本分类和网络可视化,提高与COVID-19相关的文献搜索和相关主题的效率和准确性.
主要方法:
- 利用开源机器学习工具,包括GROBID,S2ORC和BioC用于数据提取和挖掘.
- 实施了基于关键字的文本分类过程,使用RAKE,YAKE和KeyBERT对出版物数据进行分类.
- 使用NetworkX Python库来构建和Cytoscape软件来可视化出版物中的相关术语网络.
主要成果:
- KnowVID-19系统地将提取的出版数据分类为不同的主题和子主题,增强查询相关性.
- 术语协会的网络可视化允许轻松跟踪COVID-19研究中的新兴趋势.
- 该系统提供了一个交互式Web应用程序,具有直观的界面,用于无的关键字搜索,过和趋势识别.
结论:
- "了解COVID-19"显著提高了医疗研究人员访问针对性COVID-19文献的效率和准确性.
- 该系统的主题结构和网络可视化可促进以用户为中心的探索和发现科学趋势.
- KnowVID-19为导航复杂的生物医学信息环境和加速数据驱动的洞察提供了有价值的工具.
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