@文章{信息:doi/10.2196/13504，作者="杨，林，黄，李，娇"，标题="在线发现临床信息模型促进电子病历互操作性:OpenEHR的可行性研究"，期刊="J Med Internet Res"，年="2019"，月="5"，日="28"，卷="21"，数="5"，页数="e13504"，关键词=" OpenEHR;临床信息模型;卫生信息互操作性;信息检索;背景:支持语义互操作性的临床信息模型(CIMs)对于电子健康记录(EHR)数据的使用和重用至关重要。双模型方法将CIMs与技术领域区分开来，有助于实现电子病历在知识层面的互操作性。如何帮助临床医生和领域专家从在线开放存储库中发现CIMs，以标准的方式表示EHR数据变得非常重要。目的:本研究旨在开发一种在线识别CIMs以表示电子病历数据的检索方法。方法:我们提出了一种图形检索方法，并使用在线CIM存储库openEHR临床知识管理器(CKM)验证其可行性。首先，我们使用扩展的贝叶斯网络表示cim(原型)。 Then, an inference process was run in the network to discover relevant archetypes. In the evaluation, we defined three retrieval tasks (medication, laboratory test, and diagnosis) and compared our method with three typical retrieval methods (BM25F, simple Bayesian network, and CKM), using mean average precision (MAP), average precision (AP), and precision at 10 (P@10) as evaluation metrics. Results: We downloaded all available archetypes from the CKM. Then, the graphical model was applied to represent the archetypes as a four-level clinical resources network. The network consisted of 5513 nodes, including 3982 data element nodes, 504 concept nodes, 504 duplicated concept nodes, and 523 archetype nodes, as well as 9867 edges. The results showed that our method achieved the best MAP (MAP=0.32), and the AP was almost equal across different retrieval tasks (AP=0.35, 0.31, and 0.30, respectively). In the diagnosis retrieval task, our method could successfully identify the models covering ``diagnostic reports,'' ``problem list,'' ``patients background,'' ``clinical decision,'' etc, as well as models that other retrieval methods could not find, such as ``problems and diagnoses.'' Conclusions: The graphical retrieval method we propose is an effective approach to meet the uncertainty of finding CIMs. Our method can help clinicians and domain experts identify CIMs to represent EHR data in a standard manner, enabling EHR data to be exchangeable and interoperable. ", issn="1438-8871", doi="10.2196/13504", url="//www.mybigtv.com/2019/5/e13504/", url="https://doi.org/10.2196/13504", url="http://www.ncbi.nlm.nih.gov/pubmed/31140433" }