Overcoming an obstacle in expanding a UMLS semantic type extent.

Overcoming an obstacle in expanding a UMLS semantic type extent.
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DOI:
10.1016/j.jbi.2011.08.021
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发表时间:
2012-02
影响因子:
4.5
通讯作者:
Geller J
Geller J
中科院分区:
医学3区
文献类型:
--
作者:
Chen Y;Gu H;Perl Y;Geller J

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本文致力于克服一个主要问题所遇到的一个以前的扩展方法发现的概念很可能是缺少一个特定的语义类型分配的UMLS。这种方法是一种算法的基础,该算法将发现的概念呈现给人类审计员进行审查和可能的纠正。我们分析了以前的扩展方法的问题,发现这是由于一个或多个概念分配的UMLS语义网络语义类型分类构成的障碍。设计了一种新的方法,绕过了这样的障碍,而不会在提交给人类审计员进行审查的概念数量上出现组合爆炸。使用语义类型疾病实验模型测试了具有避障的新扩展方法,并发现需要这种语义类型分配的先前方法遗漏了500多个概念。此外,还发现了其他语义类型也存在同样的问题,这表明该方法具有更广泛的适用性。即使面对障碍,也可以通过算法发现可能缺少语义类型分配的概念,而不会出现处理概念数量的爆炸。
This paper strives to overcome a major problem encountered by a previous expansion methodology for discovering concepts highly likely to be missing a specific semantic type assignment in the UMLS. This methodology is the basis for an algorithm that presents the discovered concepts to a human auditor for review and possible correction. We analyzed the problem of the previous expansion methodology and discovered that it was due to an obstacle constituted by one or more concepts assigned the UMLS Semantic Network semantic type Classification. A new methodology was designed that bypasses such an obstacle without a combinatorial explosion in the number of concepts presented to the human auditor for review. The new expansion methodology with obstacle avoidance was tested with the semantic type Experimental Model of Disease and found over 500 concepts missed by the previous methodology that are in need of this semantic type assignment. Furthermore, other semantic types suffering from the same major problem were discovered, indicating that the methodology is of more general applicability. The algorithmic discovery of concepts that are likely missing a semantic type assignment is possible even in the face of obstacles, without an explosion in the number of processed concepts.
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