Pathak Jyotishman, Jiang Guoqian, Dwarkanath Sridhar O, Buntrock James D, Chute Christopher G
Division of Biomedical Informatics, Mayo Clinic College of Medicine, Rochester, MN, USA.
AMIA Annu Symp Proc. 2008 Nov 6;2008:556-60.
The ability to model, share and re-use value sets across medical information systems is an important requirement. However, generating value sets semi-automatically from a terminology service is an unresolved issue, in part due to the lack of linkage to clinical context patterns that provide the constraints in defining a concept domain and invocation of value sets extraction. Towards this goal, we develop and evaluate an approach for context-driven automatic value sets extraction based on a formal terminology model. The crux of the technique is to identify and define the context patterns from various domains of discourse and leverage them for value set extraction using two complementary ideas based on (i) local terms provided by the Subject Matter Experts (extensional) and (ii) Semantic definition of the concepts in coding schemes (intensional). A prototype was implemented based on SNOMED CT rendered in the LexGrid terminology model and a preliminary evaluation is presented.
跨医学信息系统对值集进行建模、共享和重用的能力是一项重要需求。然而，从术语服务半自动生成值集是一个尚未解决的问题，部分原因是缺乏与临床上下文模式的联系，而临床上下文模式在定义概念域和调用值集提取时提供约束。为了实现这一目标，我们开发并评估了一种基于形式术语模型的上下文驱动自动值集提取方法。该技术的关键在于从各种话语领域识别并定义上下文模式，并利用基于以下两个互补思路的值集提取方法：(i) 主题专家提供的局部术语（外延）和 (ii) 编码方案中概念的语义定义（内涵）。基于LexGrid术语模型中呈现的SNOMED CT实现了一个原型，并给出了初步评估结果。
