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利用维基数据系统提高维基百科中多种语言医学内容的质量:一项试点研究。

Utilizing the Wikidata system to improve the quality of medical content in Wikipedia in diverse languages: a pilot study.

作者信息

Pfundner Alexander, Schönberg Tobias, Horn John, Boyce Richard D, Samwald Matthias

机构信息

Section for Medical Expert and Knowledge-Based Systems, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.

出版信息

J Med Internet Res. 2015 May 5;17(5):e110. doi: 10.2196/jmir.4163.

Abstract

BACKGROUND

Wikipedia is an important source of medical information for both patients and medical professionals. Given its wide reach, improving the quality, completeness, and accessibility of medical information on Wikipedia could have a positive impact on global health.

OBJECTIVE

We created a prototypical implementation of an automated system for keeping drug-drug interaction (DDI) information in Wikipedia up to date with current evidence about clinically significant drug interactions. Our work is based on Wikidata, a novel, graph-based database backend of Wikipedia currently in development.

METHODS

We set up an automated process for integrating data from the Office of the National Coordinator for Health Information Technology (ONC) high priority DDI list into Wikidata. We set up exemplary implementations demonstrating how the DDI data we introduced into Wikidata could be displayed in Wikipedia articles in diverse languages. Finally, we conducted a pilot analysis to explore if adding the ONC high priority data would substantially enhance the information currently available on Wikipedia.

RESULTS

We derived 1150 unique interactions from the ONC high priority list. Integration of the potential DDI data from Wikidata into Wikipedia articles proved to be straightforward and yielded useful results. We found that even though the majority of current English Wikipedia articles about pharmaceuticals contained sections detailing contraindications, only a small fraction of articles explicitly mentioned interaction partners from the ONC high priority list. For 91.30% (1050/1150) of the interaction pairs we tested, none of the 2 articles corresponding to the interacting substances explicitly mentioned the interaction partner. For 7.21% (83/1150) of the pairs, only 1 of the 2 associated Wikipedia articles mentioned the interaction partner; for only 1.48% (17/1150) of the pairs, both articles contained explicit mentions of the interaction partner.

CONCLUSIONS

Our prototype demonstrated that automated updating of medical content in Wikipedia through Wikidata is a viable option, albeit further refinements and community-wide consensus building are required before integration into public Wikipedia is possible. A long-term endeavor to improve the medical information in Wikipedia through structured data representation and automated workflows might lead to a significant improvement of the quality of medical information in one of the world's most popular Web resources.

摘要

背景

维基百科是患者和医学专业人员获取医学信息的重要来源。鉴于其广泛的影响力,提高维基百科上医学信息的质量、完整性和可获取性可能对全球健康产生积极影响。

目的

我们创建了一个自动化系统的原型实现,用于使维基百科上的药物相互作用(DDI)信息与有关临床显著药物相互作用的当前证据保持一致。我们的工作基于维基数据,这是维基百科目前正在开发的一种新颖的、基于图的数据库后端。

方法

我们建立了一个自动化流程,将来自卫生信息技术国家协调办公室(ONC)高优先级DDI列表的数据集成到维基数据中。我们建立了示例实现,展示了我们引入维基数据中的DDI数据如何以多种语言在维基百科文章中显示。最后,我们进行了一项试点分析,以探讨添加ONC高优先级数据是否会大幅增强维基百科上当前可用的信息。

结果

我们从ONC高优先级列表中得出了1150种独特的相互作用。将维基数据中的潜在DDI数据集成到维基百科文章中被证明是直接的,并产生了有用的结果。我们发现,尽管当前大多数关于药品的英文维基百科文章都包含详细说明禁忌的部分,但只有一小部分文章明确提到了ONC高优先级列表中的相互作用伙伴。对于我们测试的91.30%(1050/1150)的相互作用对,与相互作用物质对应的两篇文章中没有一篇明确提到相互作用伙伴。对于7.21%(83/1150)的对,两篇相关维基百科文章中只有一篇提到了相互作用伙伴;对于仅1.48%(17/1150)的对,两篇文章都明确提到了相互作用伙伴。

结论

我们的原型表明,通过维基数据对维基百科中的医学内容进行自动更新是一个可行的选择,尽管在整合到公共维基百科之前还需要进一步完善并达成全社区的共识。通过结构化数据表示和自动化工作流程来长期努力改善维基百科中的医学信息,可能会显著提高这个世界上最受欢迎的网络资源之一的医学信息质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfe7/4468594/a5912604e57e/jmir_v17i5e110_fig1.jpg

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