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开发VISO:用于患者教育的疫苗信息声明本体。

Developing VISO: Vaccine Information Statement Ontology for patient education.

作者信息

Amith Muhammad, Gong Yang, Cunningham Rachel, Boom Julie, Tao Cui

机构信息

School of Biomedical Informatics, University of Texas Health Science Center, 7000 Fannin St, Houston, 77030 TX USA.

Immunization Project, Texas Children's Hospital, 1102 Bates, Houston, 77030 TX USA.

出版信息

J Biomed Semantics. 2015 May 1;6:23. doi: 10.1186/s13326-015-0016-2. eCollection 2015.

Abstract

OBJECTIVE

To construct a comprehensive vaccine information ontology that can support personal health information applications using patient-consumer lexicon, and lead to outcomes that can improve patient education.

METHODS

The authors composed the Vaccine Information Statement Ontology (VISO) using the web ontology language (OWL). We started with 6 Vaccine Information Statement (VIS) documents collected from the Centers for Disease Control and Prevention (CDC) website. Important and relevant selections from the documents were recorded, and knowledge triples were derived. Based on the collection of knowledge triples, the meta-level formalization of the vaccine information domain was developed. Relevant instances and their relationships were created to represent vaccine domain knowledge.

RESULTS

The initial iteration of the VISO was realized, based on the 6 Vaccine Information Statements and coded into OWL2 with Protégé. The ontology consisted of 132 concepts (classes and subclasses) with 33 types of relationships between the concepts. The total number of instances from classes totaled at 460, along with 429 knowledge triples in total. Semiotic-based metric scoring was applied to evaluate quality of the ontology.

摘要

目的

构建一个全面的疫苗信息本体,该本体能够使用患者-消费者词汇表来支持个人健康信息应用,并产生可改善患者教育的结果。

方法

作者使用网络本体语言(OWL)编写了疫苗信息声明本体(VISO)。我们从疾病控制与预防中心(CDC)网站收集的6份疫苗信息声明(VIS)文档开始。记录文档中重要且相关的内容,并得出知识三元组。基于知识三元组的集合,开发了疫苗信息领域的元级形式化。创建了相关实例及其关系来表示疫苗领域知识。

结果

基于6份疫苗信息声明实现了VISO的初始迭代,并使用Protégé编码为OWL2。该本体由132个概念(类和子类)组成,概念之间有33种关系类型。类的实例总数为460个,总共还有429个知识三元组。应用基于符号学的度量评分来评估本体的质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aef6/4429537/655d3936c29f/13326_2015_16_Fig1_HTML.jpg

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