SNOMED CT is the most comprehensive clinical ontology and is also amenable for automated reasoning. However, in order to unleash its full potential for automated reasoning over clinical text, a mechanism to convert clinical terms into SNOMED CT concepts is necessary. In this paper we present, to the best of our knowledge, the first such complete conversion method that is also capable of converting clinical terms into post-coordinated concepts which are not already listed in SNOMED CT. The method does not require any additional manual annotations and learns only from existing SNOMED CT terms paired with their concepts. The method is based on identifying the defining relations of the clinical concept expressed by a clinical term. We evaluate our method on a large-scale using existing data from SNOMED CT as well as on a small-scale using manually annotated dataset of clinical terms found in clinical text.

SNOMED CT是最全面的临床本体，也适用于自动推理。但是，为了释放其通过临床文本进行自动推理的全部潜能，需要一种将临床术语转换为SNOMED CT概念的机制。在本文中，我们尽我们所能介绍了第一种完全转换方法，该方法也能够将临床术语转换为SNOMED CT中尚未列出的后协调概念。该方法不需要任何其他手动注释，只能从现有的SNOMED CT术语及其概念中学习。该方法基于识别由临床术语表达的临床概念的定义关系。