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Automatic Definition of the Oncologic EHR Data Elements from NCIT in OWL

机译：从NCIT中自动定义OWL中NCIT的盲肠ehr数据元素

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Semantic interoperability based on ontologies allows systems to combine their information and process them automatically. The ability to extract meaningful fragments from ontology is a key for the ontology re-use and the construction of a subset will help to structure clinical data entries. The aim of this work is to provide a method for extracting a set of concepts for a specific domain, in order to help to define data elements of an oncologic EHR. Method: a generic extraction algorithm was developed to extract, from the NCIT and for a specific disease (i.e. prostate neoplasm), all the concepts of interest into a sub-ontology. We compared all the concepts extracted to the concepts encoded manually contained into the multi-disciplinary meeting report form (MDMRF). Results: We extracted two sub-ontologies: sub-ontology 1 by using a single key concept and sub-ontology 2 by using 5 additional ^g>The coverage of sub-ontology 2 to the MDMRF concepts was 51%. The low rate of coverage is due to the lack of definition or mis-classification of the NCIT concepts. By providing a subset of concepts focused on a particular domain, this extraction method helps at optimizing the binding process of data elements and at maintaining and enriching a domain ontology.
机译：基于本体的语义互操作性允许系统将其信息组合并自动处理它们。从本体中提取有意义的片段的能力是本体重复使用的关键，并且子集的构造将有助于构建临床数据条目。这项工作的目的是提供一种用于提取特定域的一组概念的方法，以帮助定义肿瘤ehr的数据元素。方法：开发了一种通用提取算法以从NCIT和特定疾病中提取（即前列腺肿瘤），所有感兴趣的概念才能进入次题。我们将提取的所有概念与手动包含在多学科会议报告表（MDMRF）中的编码中的概念进行了比较。结果：我们通过使用单个关键概念和子本体学2附加^ G>将子本体概念2的覆盖率提取了两个子本体论：子本体学1，占地面积2对MDMRF概念的覆盖率为51％。覆盖率的低速率是由于NCIT概念的定义或错误分类。通过提供专注于特定域的概念子集，该提取方法有助于优化数据元素的绑定过程和维持和富集域本体。

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