Shahar Y, Cheng C
Stanford Medical Informatics, School of Medicine, Stanford University, CA, USA.
Proc AMIA Symp. 1998:155-9.
We describe a domain-independent framework (KNAVE) specific to the task of interpretation, summarization, visualization, explanation, and interactive exploration in a context-sensitive manner through time-oriented raw clinical data and the multiple levels of higher-level, interval-based concepts that can be abstracted from these data. The KNAVE exploration operators, which are independent of any particular clinical domain, access a knowledge base of temporal properties of measured data and interventions that is specific to the clinical domain. Thus, domain-specific knowledge underlies the domain-independent semantics of the interpretation, visualization, and exploration processes. Initial evaluation of the KNAVE prototype by a small number of users with variable clinical and informatics training has been encouraging.
我们描述了一个与领域无关的框架(KNAVE),该框架通过面向时间的原始临床数据以及可从这些数据中抽象出来的多层次、基于时间间隔的高级概念,以情境敏感的方式专门用于解释、总结、可视化、解释和交互式探索任务。KNAVE探索运算符独立于任何特定临床领域,可访问特定于临床领域的测量数据和干预措施的时间属性知识库。因此,特定领域的知识构成了解释、可视化和探索过程的与领域无关的语义基础。少数具有不同临床和信息学培训背景的用户对KNAVE原型进行的初步评估结果令人鼓舞。