Yang Lin, Huang Xiaoshuo, Hou Li, Qian Qing, Li Jiao
Institute of Medical Information and Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China.
Stud Health Technol Inform. 2019 Aug 21;264:1622-1623. doi: 10.3233/SHTI190565.
To facilitate experts to find relevant clinical information models, we took a case study of openEHR. We proposed to use a graphical model to represent EHR archetype sets, aiming to optimize clincal information retrieval performance. In this study, we applied our graphic model to 523 OpenEHR archetypes and represented them as a graph with 5,008 nodes and 6,908 edges, which consists of 3,982 term nodes, 504 concept nodes, and 523 archetype nodes. On basis of the graphical model, it improved the performance for retrieving the clinical queries.
为便于专家找到相关临床信息模型，我们以openEHR为例进行了研究。我们提议使用图形模型来表示电子健康记录原型集，旨在优化临床信息检索性能。在本研究中，我们将图形模型应用于523个OpenEHR原型，并将它们表示为一个具有5008个节点和6908条边的图，该图由3982个术语节点、504个概念节点和523个原型节点组成。基于该图形模型，它提高了临床查询的检索性能。
