Redjdal Akram, Bouaud Jacques, Guézennec Gilles, Gligorov Joseph, Seroussi Brigitte
Sorbonne Université, Université Sorbonne Paris Nord, INSERM, UMR S_1142, LIMICS, Paris, France.
AP-HP, DRCI, Paris, France.
Stud Health Technol Inform. 2020 Nov 23;275:177-181. doi: 10.3233/SHTI200718.
Interoperability issues are common in biomedical informatics. Reusing data generated from a system in another system, or integrating an existing clinical decision support system (CDSS) in a new organization is a complex task due to recurrent problems of concept mapping and alignment. The GL-DSS of the DESIREE project is a guideline-based CDSS to support the management of breast cancer patients. The knowledge base is formalized as an ontology and decision rules. OncoDoc is another CDSS applied to breast cancer management. The knowledge base is structured as a decision tree. OncoDoc has been routinely used by the multidisciplinary tumor board physicians of the Tenon Hospital (Paris, France) for three years leading to the resolution of 1,861 exploitable decisions. Because we were lacking patient data to assess the DESIREE GL-DSS, we investigated the option of reusing OncoDoc patient data. Taking into account that we have two CDSSs with two formalisms to represent clinical practice guidelines and two knowledge representation models, we had to face semantic and structural interoperability issues. This paper reports how we created 10,681 synthetic patients to solve these issues and make OncoDoc data re-usable by the GL-DSS of DESIREE.
互操作性问题在生物医学信息学中很常见。在另一个系统中复用从一个系统生成的数据，或者在一个新机构中集成现有的临床决策支持系统（CDSS），由于概念映射和对齐方面反复出现的问题，是一项复杂的任务。DESIREE项目的GL-DSS是一个基于指南的CDSS，用于支持乳腺癌患者的管理。知识库被形式化为一个本体和决策规则。OncoDoc是另一个应用于乳腺癌管理的CDSS。知识库被构建为一个决策树。OncoDoc已经被法国巴黎特农医院的多学科肿瘤委员会医生常规使用了三年，导致1861个可利用决策得到解决。由于我们缺乏评估DESIREE GL-DSS的患者数据，我们研究了复用OncoDoc患者数据的选项。考虑到我们有两个具有两种表示临床实践指南的形式主义和两种知识表示模型的CDSS，我们不得不面对语义和结构互操作性问题。本文报告了我们如何创建10681个合成患者来解决这些问题，并使OncoDoc数据能够被DESIREE的GL-DSS复用。
