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首页> 外文期刊>BMC Bioinformatics >Enhancing the drug ontology with semantically-rich representations of National Drug Codes and RxNorm unique concept identifiers
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Enhancing the drug ontology with semantically-rich representations of National Drug Codes and RxNorm unique concept identifiers

机译：用语义上丰富的国家药物代码和rxnorm独特概念标识符增强药物本体论

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BACKGROUND:The Drug Ontology (DrOn) is a modular, extensible ontology of drug products, their ingredients, and their biological activity created to enable comparative effectiveness and health services researchers to query National Drug Codes (NDCs) that represent products by ingredient, by molecular disposition, by therapeutic disposition, and by physiological effect (e.g., diuretic). It is based on the RxNorm drug terminology maintained by the U.S. National Library of Medicine, and on the Chemical Entities of Biological Interest ontology. Both national drug codes (NDCs) and RxNorm unique concept identifiers (RXCUIS) can undergo changes over time that can obfuscate their meaning when these identifiers occur in historic data. We present a new approach to modeling these entities within DrOn that will allow users of DrOn working with historic prescription data to more easily and correctly interpret that data.RESULTS:We have implemented a full accounting of national drug codes and RxNorm unique concept identifiers as information content entities, and of the processes involved in managing their creation and changes. This includes an OWL file that implements and defines the classes necessary to model these entities. A separate file contains an instance-level prototype in OWL that demonstrates the feasibility of this approach to representing NDCs and RXCUIs and the processes of managing them by retrieving and representing several individual NDCs, both active and inactive, and the RXCUIs to which they are connected. We also demonstrate how historic information about these identifiers in DrOn can be easily retrieved using a simple SPARQL query.CONCLUSIONS:An accurate model of how these identifiers operate in reality is a valuable addition to DrOn that enhances its usefulness as a knowledge management resource for working with historic data.
机译：背景：药物本体（DRON）是药品，其成分的模块化，可伸展的本体论，以及其生物活动，以使比较有效性和卫生服务研究人员通过分子来查询代表产品的国家药物代码（NDC）通过治疗性处理和生理效应（例如，利尿剂）进行处理。它基于由美国国家医学图书馆维护的Rxnorm药物术语，以及生物利益本体的化学实体。全国药物代码（NDC）和RxNorm独特的概念标识符（RXCUIS）可以随时间进行变化，当这些标识符发生在历史数据中时，可以混淆它们的含义。我们提出了一种新的方法，可以在Dron中建模这些实体，允许Dron的用户与历史处方数据一起工作，以更容易和正确地解释该数据。结果：我们已实施全国药物代码和Rxnorm独特概念标识符的完整会计内容实体，以及管理创建和更改所涉及的流程。这包括一个猫头鹰文件，它可以实现并定义为模拟这些实体所需的类。单独的文件包含OWL中的实例级原型，展示了这种方法来表示NDC和RXCUI的可行性以及通过检索和表示多个单独的NDC来管理它们的过程，都是活动和非活动的，以及它们所连接的RXCUIS 。我们还通过简单的SPARQL查询来展示关于DRON中这些标识符中这些标识符的历史信息.Conclusions：这些标识符如何在现实中运行的准确模型是默顿的有价值的补充，可以增强其作为工作的知识管理资源的有用性与历史数据。

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