选择性检索前后协调的SNOMED概念。

Proceedings. AMIA Symposium Pub Date : 2002-01-01
Robert H Dolin, Kent A Spackman, David Markwell
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引用次数: 0

摘要

一般来说,在电子医疗记录中存储概念标识符并在消息中表示它们是非常简单的。信息模型通常指定可以包含编码条目的字段。对于这些字段中的每一个,可能都有额外的约束来精确地控制哪些概念标识符是适用的。但是,由于现代术语(如SNOMED CT)是组合的,允许在术语中预先协调概念表达式,或在医疗记录中进行后协调,因此仍然存在以多种方式表达概念的可能性。通常情况下,各种表示是相似的,但并不等同。本文描述了一种检索这些前后协调的概念表达式的方法:(1)根据明确指定的信息模型(本文使用HL7 RIM)的规则,使用逻辑结构良好的术语(例如,SNOMED CT)创建概念表达式;(2)将前后协调的概念表达式转化为规格化形式;(3)将查询转换为相同的规范化形式。然后可以直接将规范化的实例与查询进行比较。已经确定了几个执行方面的考虑。需要优化到标准形式的转换和需要遍历层次结构的查询的执行。详细了解信息模型和术语模型是先决条件。基于概念的语义属性的查询仅与术语模型中包含的语义信息一样完整。尽管有这些考虑,但这种方法似乎很强大,并将继续改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Selective retrieval of pre- and post-coordinated SNOMED concepts.

In general, it is very straightforward to store concept identifiers in electronic medical records and represent them in messages. Information models typically specify the fields that can contain coded entries. For each of these fields there may be additional constraints governing exactly which concept identifiers are applicable. However, because modern terminologies such as SNOMED CT are compositional, allowing concept expressions to be pre-coordinated within the terminology or post-coordinated within the medical record, there remains the potential to express a concept in more than one way. Often times, the various representations are similar, but not equivalent. This paper describes an approach for retrieving these pre- and post-coordinated concept expressions: (1) Create concept expressions using a logically-well-structured terminology (e.g., SNOMED CT) according to the rules of a well-specified information model (in this paper we use the HL7 RIM); (2) Transform pre- and post-coordinated concept expressions into a normalized form; (3) Transform queries into the same normalized form. The normalized instances can then be directly compared to the query. Several implementation considerations have been identified. Transformations into a normal form and execution of queries that require traversal of hierarchies need to be optimized. A detailed understanding of the information model and the terminology model are prerequisites. Queries based on the semantic properties of concepts are only as complete as the semantic information contained in the terminology model. Despite these considerations, the approach appears powerful and will continue to be refined.

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