Using rule-based natural language processing to improve disease normalization in biomedical text.
Using rule-based natural language processing to improve disease normalization in biomedical text.
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DOI：
10.1136/amiajnl-2012-001173
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发表时间：
2013-09
期刊：
影响因子：
--
通讯作者：
Kors JA
中科院分区：
文献类型：
--
作者：
Kang N;Singh B;Afzal Z;van Mulligen EM;Kors JA
为了使计算机从非结构化文本中提取有用的信息，需要一个概念规范化系统来将文本中的相关概念与包含有关概念的进一步信息的源联系起来。生物医学领域流行的概念规范化工具是基于字典的。在这项研究中，我们调查的有用性，自然语言处理（NLP）作为一种辅助工具，以字典为基础的概念规范化。我们比较了两个生物医学概念规范化系统，MetaMap和Peregrine，亚利桑那州疾病语料库的性能，使用和不使用基于规则的NLP模块。性能进行了评估，准确和不准确的边界匹配的系统注释与金标准和概念标识符匹配。在没有NLP模块的情况下，MetaMap和Peregrine在精确边界匹配方面的F分数分别为61.0%和63.9%，在概念标识符匹配方面的F分数分别为55.1%和56.9%。在NLP模块的帮助下，MetaMap和Peregrine的边界匹配F分数分别提高到73.3%和78.0%，概念标识符匹配F分数分别提高到66.2%和69.8%。对于不精确的边界匹配，性能进一步提高到85.5%和85.4%，并为73.6%和73.3%的概念标识符匹配。我们已经展示了NLP在MetaMap和Peregrine疾病识别和标准化方面的附加价值。NLP模块是通用的，可以与任何概念规范化系统结合使用。它用于疾病以外的概念类型是否同样有利还有待研究。
In order for computers to extract useful information from unstructured text, a concept normalization system is needed to link relevant concepts in a text to sources that contain further information about the concept. Popular concept normalization tools in the biomedical field are dictionary-based. In this study we investigate the usefulness of natural language processing (NLP) as an adjunct to dictionary-based concept normalization. We compared the performance of two biomedical concept normalization systems, MetaMap and Peregrine, on the Arizona Disease Corpus, with and without the use of a rule-based NLP module. Performance was assessed for exact and inexact boundary matching of the system annotations with those of the gold standard and for concept identifier matching. Without the NLP module, MetaMap and Peregrine attained F-scores of 61.0% and 63.9%, respectively, for exact boundary matching, and 55.1% and 56.9% for concept identifier matching. With the aid of the NLP module, the F-scores of MetaMap and Peregrine improved to 73.3% and 78.0% for boundary matching, and to 66.2% and 69.8% for concept identifier matching. For inexact boundary matching, performances further increased to 85.5% and 85.4%, and to 73.6% and 73.3% for concept identifier matching. We have shown the added value of NLP for the recognition and normalization of diseases with MetaMap and Peregrine. The NLP module is general and can be applied in combination with any concept normalization system. Whether its use for concept types other than disease is equally advantageous remains to be investigated.
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影响因子：
3
作者：
Ratkovic Z;Golik W;Warnier P
通讯作者：
Warnier P
DOI：
10.1093/bioinformatics/btq129
发表时间：
2010-05-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Okazaki N;Ananiadou S;Tsujii J
通讯作者：
Tsujii J
影响因子：
14.9
作者：
Harris, MA;Clark, J;White, R
通讯作者：
White, R
DOI：
10.1109/icimw.2009.5324773
发表时间：
2009-01-01
期刊：
METHODS IN BIOENGINEERING: SYSTEMS ANALYSIS OF BIOLOGICAL NETWORKS
影响因子：
--
作者：
Kim, Jin-Hong;Asthagiri, Anand R.
通讯作者：
Asthagiri, Anand R.
生物公约概述：生物学信息提取的批判性评估。
影响因子：
3
作者：
Hirschman L;Yeh A;Blaschke C;Valencia A
通讯作者：
Valencia A