Department of Digital Health, Rouen University Hospital, Rouen, France; Department of Pharmacy, Rouen University Hospital, Rouen, France; Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé (LIMICS), U1142, INSERM, Sorbonne Université, Paris, France.
Department of Digital Health, Rouen University Hospital, Rouen, France; Department of Pharmacy, Rouen University Hospital, Rouen, France.
J Biomed Inform. 2023 Apr;140:104325. doi: 10.1016/j.jbi.2023.104325. Epub 2023 Mar 2.
Monoclonal antibodies (MAs) are increasingly used in the therapeutic arsenal. Clinical Data Warehouses (CDWs) offer unprecedented opportunities for research on real-word data. The objective of this work is to develop a knowledge organization system on MAs for therapeutic use (MATUs) applicable in Europe to query CDWs from a multi-terminology server (HeTOP). After expert consensus, three main health thesauri were selected: the MeSH thesaurus, the National Cancer Institute thesaurus (NCIt) and the SNOMED CT. These thesauri contain 1,723 MAs concepts, but only 99 (5.7 %) are identified as MATUs. The knowledge organisation system proposed in this article is a six-level hierarchical system according to their main therapeutic target. It includes 193 different concepts organised in a cross lingual terminology server, which will allow the inclusion of semantic extensions. Ninety nine (51.3 %) MATUs concepts and 94 (48.7 %) hierarchical concepts composed the knowledge organisation system. Two separates groups (an expert group and a validation group) carried out the selection, creation and validation processes. Queries identify, for unstructured data, 83 out of 99 (83.8 %) MATUs corresponding to 45,262 patients, 347,035 hospital stays and 427,544 health documents, and for structured data, 61 out of 99 (61.6 %) MATUs corresponding to 9,218 patients, 59,643 hospital stays and 104,737 hospital prescriptions. The volume of data in the CDW demonstrated the potential for using these data in clinical research, although not all MATUs are present in the CDW (16 missing for unstructured data and 38 for structured data). The knowledge organisation system proposed here improves the understanding of MATUs, the quality of queries and helps clinical researchers retrieve relevant medical information. The use of this model in CDW allows for the rapid identification of a large number of patients and health documents, either directly by a MATU of interest (e.g. Rituximab) but also by searching for parent concepts (e.g. Anti-CD20 Monoclonal Antibody).
单克隆抗体 (MA) 在治疗武器库中越来越多地被使用。临床数据仓库 (CDW) 为真实世界数据的研究提供了前所未有的机会。这项工作的目的是为欧洲治疗用途的 MA 开发一个知识组织系统 (MATU)，以便从多术语服务器 (HeTOP) 查询 CDW。在专家达成共识后，选择了三个主要的健康词库：MeSH 词库、国家癌症研究所词库 (NCIt) 和 SNOMED CT。这些词库包含 1723 个 MA 概念，但只有 99 个 (5.7%) 被确定为 MATU。本文提出的知识组织系统是一个六级分层系统，根据其主要治疗目标进行分类。它包括 193 个不同的概念，组织在一个跨语言术语服务器中，这将允许语义扩展的加入。99 个 (51.3%) MATU 概念和 94 个 (48.7%) 分层概念组成了知识组织系统。两个独立的小组 (专家组和验证组) 进行了选择、创建和验证过程。查询从非结构化数据中识别出 99 个 (83.8%) MATU 中的 83 个，对应 45262 名患者、347035 次住院和 427544 份健康文件，从结构化数据中识别出 99 个 (61.6%) MATU 中的 61 个，对应 9218 名患者、59643 次住院和 104737 张医院处方。CDW 中的数据量表明了在临床研究中使用这些数据的潜力，尽管并非所有的 MATU 都存在于 CDW 中 (非结构化数据中缺少 16 个，结构化数据中缺少 38 个)。本文提出的知识组织系统提高了对 MATU 的理解、查询的质量，并帮助临床研究人员检索相关的医疗信息。在 CDW 中使用这种模式可以快速识别大量的患者和健康文件，既可以直接通过感兴趣的 MATU (例如利妥昔单抗)，也可以通过搜索父概念 (例如抗 CD20 单克隆抗体)。
