基于图的医学信息检索概念加权

B. Koopman, G. Zuccon, P. Bruza, Laurianne Sitbon, Michael Lawley
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引用次数: 26

摘要

本文提出了一种基于图的医学概念权重方法,用于信息检索。医学概念是从自由文本文档中提取的,使用最先进的技术,将n-图映射到SNOMED CT医学本体的概念。在我们基于图的概念表示中,概念是由文档构建的图中的顶点,边表示概念之间的关联。这种表示自然地捕获了概念之间的依赖关系,这是解释医学文本的一个重要要求,也是词袋表示中缺乏的一个特性。我们将现有的基于图的术语加权方法应用于医学概念的加权。使用概念而不是术语可以解决词汇表不匹配的问题,并将属于单个医疗实体的术语封装到单个概念中。此外,我们通过注入领域知识进一步扩展了以前基于图的方法,这些领域知识可以估计一个概念在全球医学领域中的重要性。在TREC医疗记录集合上的检索实验表明,我们的方法优于术语基线和概念基线。更一般地说,这项工作提供了一种将医学本体中包含的背景知识集成到数据驱动的信息检索方法中的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graph-based concept weighting for medical information retrieval
This paper presents a graph-based method to weight medical concepts in documents for the purposes of information retrieval. Medical concepts are extracted from free-text documents using a state-of-the-art technique that maps n-grams to concepts from the SNOMED CT medical ontology. In our graph-based concept representation, concepts are vertices in a graph built from a document, edges represent associations between concepts. This representation naturally captures dependencies between concepts, an important requirement for interpreting medical text, and a feature lacking in bag-of-words representations. We apply existing graph-based term weighting methods to weight medical concepts. Using concepts rather than terms addresses vocabulary mismatch as well as encapsulates terms belonging to a single medical entity into a single concept. In addition, we further extend previous graph-based approaches by injecting domain knowledge that estimates the importance of a concept within the global medical domain. Retrieval experiments on the TREC Medical Records collection show our method outperforms both term and concept baselines. More generally, this work provides a means of integrating background knowledge contained in medical ontologies into data-driven information retrieval approaches.
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