NCBI disease corpus: a resource for disease name recognition and concept normalization.
NCBI disease corpus: a resource for disease name recognition and concept normalization.
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DOI：
10.1016/j.jbi.2013.12.006
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发表时间：
2014-02
影响因子：
4.5
通讯作者：
Lu, Zhiyong
中科院分区：
文献类型：
--
作者：
Dogan, Rezarta Islamaj;Leaman, Robert;Lu, Zhiyong
关键词：
生物医学文献出版物中以自然语言编码的信息只有在有效和可靠的访问和分析信息的方法可用时才有用。因此，自然语言处理和文本挖掘工具对于提取有价值的信息至关重要，然而，开发强大，高效的工具来自动检测疾病等中心生物医学概念是有条件的。本文介绍了NCBI疾病语料库的疾病名称和概念注释，该语料库收集了793篇PubMed摘要，在提及和概念层面进行了充分注释，以作为生物医学自然语言处理社区的研究资源。每个PubMed摘要都由两名注释者手动注释，其中疾病提及及其在医学主题词（MeSH®）或在线孟德尔遗传人类（OMIM®）中的相应概念。使用PubTator执行手动管理，其允许使用预注释作为手动注释的预步骤。随机配对了14名注释者，并讨论了不同的注释，以便在两个注释阶段达成共识。在这种情况下，观察到注释者之间的高度一致性。最后，所有结果都与语料库的其余部分的注释进行了检查，以确保语料库范围内的一致性。NCBI疾病语料库的公开发布包含6，892个疾病提及，这些疾病被映射到790个独特的疾病概念。其中，88%链接到MeSH标识符，其余包含OMIM标识符。我们能够将91%的提及与单一疾病概念联系起来，而其余的则被描述为概念的组合。为了帮助研究人员使用语料库设计和测试疾病识别方法，我们准备了语料库作为训练，测试和开发集。为了证明其实用性，我们进行了一个基准测试实验，比较了三种不同的基于知识的疾病标准化方法，F-测量的最佳性能为63.7%。这些结果表明，NCBI疾病语料库有可能通过提供高质量的黄金标准，从而为此类任务开发基于机器学习的方法，从而显着提高疾病名称识别和规范化研究的最新水平。
Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information, however, the development of powerful, highly effective tools to automatically detect central biomedical concepts such as diseases is conditional on the availability of annotated corpora. This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency. The public release of the NCBI disease corpus contains 6,892 disease mentions, which are mapped to 790 unique disease concepts. Of these, 88% link to a MeSH identifier, while the rest contain an OMIM identifier. We were able to link 91% of the mentions to a single disease concept, while the rest are described as a combination of concepts. In order to help researchers use the corpus to design and test disease identification methods, we have prepared the corpus as training, testing and development sets. To demonstrate its utility, we conducted a benchmarking experiment where we compared three different knowledge-based disease normalization methods with a best performance in F-measure of 63.7%. These results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.
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影响因子：
3
作者：
Bada M;Eckert M;Evans D;Garcia K;Shipley K;Sitnikov D;Baumgartner WA Jr;Cohen KB;Verspoor K;Blake JA;Hunter LE
通讯作者：
Hunter LE
影响因子：
3
作者：
Jimeno A;Jimenez-Ruiz E;Lee V;Gaudan S;Berlanga R;Rebholz-Schuhmann D
通讯作者：
Rebholz-Schuhmann D
影响因子：
4.3
作者：
Cohen KB;Hunter L
通讯作者：
Hunter L
DOI：
10.1056/nejmp0900702
发表时间：
2009-05-21
期刊：
The New England journal of medicine
影响因子：
--
作者：
Brownstein JS;Freifeld CC;Madoff LC
通讯作者：
Madoff LC
DOI：
10.1007/978-1-60327-194-3_16
发表时间：
2010-01-01
期刊：
BIOINFORMATICS METHODS IN CLINICAL RESEARCH
影响因子：
--
作者：
Krallinger, Martin;Leitner, Florian;Valencia, Alfonso
通讯作者：
Valencia, Alfonso