Improving search over Electronic Health Records using UMLS-based query expansion through random walks
Improving search over Electronic Health Records using UMLS-based query expansion through random walks
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DOI：
10.1016/j.jbi.2014.04.013
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发表时间：
2014-10-01
影响因子：
4.5
通讯作者：
Agirre, Eneko
中科院分区：
文献类型：
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作者：
Martinez, David;Otegi, Arantxa;Agirre, Eneko
目标：电子健康记录 (EHR) 中的大部分信息均以自由文本形式表示。搜索电子病历的从业者需要仔细表达他们的查询，因为记录可能使用同义词或其他相关词。在本文中，我们展示了基于统一医学语言系统 (UMLS) 元同义词库的自动查询扩展方法，在搜索 EHR 时改进了稳健基线的结果。 材料和方法：该方法使用 UMLS 元同义词库中词汇单元、概念和关系的图形表示。它基于图表上的随机游走，从查询项开始。随机游走是 Web 和知识库数据集中经过深入研究的学科。结果：我们在 TREC 医疗记录轨道上进行的实验显示，2011 年和 2012 年的数据集在强大的基线上都有所改进。讨论：我们的分析表明，我们方法的成功是由于使用额外术语自动扩展查询，即使它们在 UMLS Metathesaurus 中不直接相关。扩展中添加的术语超出了简单同义词的范围，还添加了其他类型的主题相关术语。 结论：使用 UMLS Metathesaurus 中的相关术语扩展同义词之外的查询是在搜索患者队列时克服查询和文档词汇之间差距的有效方法。 (C) 2014 Elsevier Inc. 保留所有权利。
Objective: Most of the information in Electronic Health Records (EHRs) is represented in free textual form. Practitioners searching EHRs need to phrase their queries carefully, as the record might use synonyms or other related words. In this paper we show that an automatic query expansion method based on the Unified Medicine Language System (UMLS) Metathesaurus improves the results of a robust baseline when searching EHRs.Materials and methods: The method uses a graph representation of the lexical units, concepts and relations in the UMLS Metathesaurus. It is based on random walks over the graph, which start on the query terms. Random walks are a well-studied discipline in both Web and Knowledge Base datasets.Results: Our experiments over the TREC Medical Record track show improvements in both the 2011 and 2012 datasets over a strong baseline. Discussion: Our analysis shows that the success of our method is due to the automatic expansion of the query with extra terms, even when they are not directly related in the UMLS Metathesaurus. The terms added in the expansion go beyond simple synonyms, and also add other kinds of topically related terms.Conclusions: Expansion of queries using related terms in the UMLS Metathesaurus beyond synonymy is an effective way to overcome the gap between query and document vocabularies when searching for patient cohorts. (C) 2014 Elsevier Inc. All rights reserved.