Toward a complete dataset of drug-drug interaction information from publicly available sources.
Toward a complete dataset of drug-drug interaction information from publicly available sources.
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DOI：
10.1016/j.jbi.2015.04.006
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发表时间：
2015-06
影响因子：
4.5
通讯作者：
Boyce, Richard D.
中科院分区：
文献类型：
--
作者：
Ayvaz, Serkan;Horn, John;Hassanzadeh, Oktie;Zhu, Qian;Stan, Johann;Tatonetti, Nicholas P.;Vilar, Santiago;Brochhausen, Mathias;Samwald, Matthias;Rastegar-Mojarad, Majid;Dumontier, Michel;Boyce, Richard D.
虽然潜在的药物-药物相互作用（PDDI）是可预防的药物相关伤害的重要来源，但目前还没有单一的完整的PDDI信息来源。在目前的研究中，所有可以通过全面和广泛的搜索识别的公开可用的PDDI信息来源被组合成一个单一的数据集。合并后的数据集合并了14个不同的信息源，包括5个临床信息源、4个自然语言处理（NLP）语料库和5个生物信息学/药物警戒信息源。作为一个全面的PDDI源，合并后的数据集可能有利于药物警戒文本挖掘社区，因为它可以比较NLP语料库在PDDI文本提取任务中的代表性，并指定对未来PDDI提取目的有用的元素。对数据来源之间和之间的重叠进行的分析表明，几乎没有重叠。即使是综合的PDDI列表，如DrugBank、KEGG和NDF-RT，彼此之间的重叠也不到50%。此外，所有的综合清单都没有完全覆盖两个数据来源，这两个数据来源主要关注大多数临床环境中感兴趣的pddi。基于这些信息，我们认为提供访问综合列表的系统，例如RxNorm中的api，应该小心地告知用户，关于药物专家建议临床医生注意的pddi列表可能是不完整的。尽管重叠程度较低，但仍发现了几十个病例，其中药品标签中提供的PDDI信息可能会通过合并的数据集得到增强。此外，该组合数据集还被证明可以提高现有PDDI NLP管道和最近发布的PDDI药物警戒方案的性能。未来的工作将集中在改进PDDI信息源之间的映射方法，确定改进PDDI NLP算法中合并数据集使用的方法，将合并数据集中的高质量PDDI信息集成到Wikidata中，并使合并数据集作为语义Web关联数据可访问。
Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The combined dataset merged fourteen different sources including 5 clinically-oriented information sources, 4 Natural Language Processing (NLP) Corpora, and 5 Bioinformatics/Pharmacovigilance information sources. As a comprehensive PDDI source, the merged dataset might benefit the pharmacovigilance text mining community by making it possible to compare the representativeness of NLP corpora for PDDI text extraction tasks, and specifying elements that can be useful for future PDDI extraction purposes. An analysis of the overlap between and across the data sources showed that there was little overlap. Even comprehensive PDDI lists such as DrugBank, KEGG, and the NDF-RT had less than 50% overlap with each other. Moreover, all of the comprehensive lists had incomplete coverage of two data sources that focus on PDDIs of interest in most clinical settings. Based on this information, we think that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. In spite of the low degree of overlap, several dozen cases were identified where PDDI information provided in drug product labeling might be augmented by the merged dataset. Moreover, the combined dataset was also shown to improve the performance of an existing PDDI NLP pipeline and a recently published PDDI pharmacovigilance protocol. Future work will focus on improvement of the methods for mapping between PDDI information sources, identifying methods to improve the use of the merged dataset in PDDI NLP algorithms, integrating high-quality PDDI information from the merged dataset into Wikidata, and making the combined dataset accessible as Semantic Web Linked Data.
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影响因子：
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作者：
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通讯作者：
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