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使用形式概念分析在临床领域中基于上下文的本体构建支持

Context-based ontology building support in clinical domains using formal concept analysis.

作者信息

Jiang Guoqian, Ogasawara Katsuhiko, Endoh Akira, Sakurai Tsunetaro

机构信息

Department of Medical Informatics, Hokkaido University Graduate School of Medicine, North 15, West 7, Kita-ku, Sapporo 060-8638, Japan.

出版信息

Int J Med Inform. 2003 Aug;71(1):71-81. doi: 10.1016/s1386-5056(03)00092-3.

Abstract

OBJECTIVE

Ontology in clinical domains is becoming a core research field in the realm of medical informatics. The objective of this study is to explore the potential role of formal concept analysis (FCA) in a context-based ontology building support in a clinical domain (e.g. cardiovascular medicine here).

METHODOLOGY

We developed an ontology building support system that integrated an FCA module with a natural language processing (NLP) module. The user interface of the system was developed as a Protégé-2000 JAVA tab plug-in. A collection of 368 textual discharge summaries and a standard dictionary of Japanese diagnostic terms (MEDIS ver2.0) were used as the main knowledge sources. A preliminary evaluation was taken to show the usefulness of the system.

RESULTS

Stability was shown on the MEDIS-based medical concept extraction with high precision. 73+/-14% (mean+/-S.D.) of the compound medical phrases extracted were sufficiently meaningful to form a medical concept from a clinical perspective. Also, 57.7% of attribute implication pairs (i.e. medical concept pairs) extracted were identified as positive from a clinical perspective.

CONCLUSION

Under the framework of our ontology building support system using FCA, the clinical experts could reach a mass of both linguistic information and context-based knowledge that was demonstrated as useful to support their ontology building tasks.

摘要

目的

临床领域的本体论正成为医学信息学领域的一个核心研究方向。本研究的目的是探讨形式概念分析(FCA)在临床领域(如本文中的心血管医学)基于上下文的本体构建支持中的潜在作用。

方法

我们开发了一个本体构建支持系统,该系统将FCA模块与自然语言处理(NLP)模块集成在一起。系统的用户界面是作为Protégé - 2000 JAVA标签插件开发的。收集了368份文本出院小结和一本日本诊断术语标准词典(MEDIS ver2.0)作为主要知识来源。进行了初步评估以证明该系统的实用性。

结果

在基于MEDIS的医学概念提取方面显示出稳定性,精度较高。从临床角度来看,所提取的复合医学短语中有73±14%(平均值±标准差)具有足够的意义来形成一个医学概念。此外,从临床角度来看,所提取的属性蕴含对(即医学概念对)中有57.7%被确定为正向的。

结论

在我们使用FCA的本体构建支持系统框架下,临床专家能够获取大量语言信息和基于上下文的知识,这些知识被证明对支持他们的本体构建任务很有用。

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