Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
J Biomed Inform. 2020 Nov;111:103585. doi: 10.1016/j.jbi.2020.103585. Epub 2020 Oct 2.
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，也适用于自动化推理。然而，为了充分发挥其在临床文本上进行自动化推理的潜力，需要有一种将临床术语转换为 SNOMED CT 概念的机制。在本文中，我们提出了据我们所知的第一个完整的转换方法，该方法还能够将临床术语转换为不在 SNOMED CT 中列出的后协调概念。该方法不需要任何额外的手动注释，仅从现有的 SNOMED CT 术语及其概念中学习。该方法基于识别临床术语所表达的临床概念的定义关系。我们使用现有的 SNOMED CT 数据以及从临床文本中手动注释的临床术语数据集在大规模和小规模上评估了我们的方法。
