Duke Jon D, Friedlin Jeff
Regenstrief Institute, Indianapolis, IN;
AMIA Annu Symp Proc. 2010 Nov 13;2010:177-81.
Evaluating medications for potential adverse events is a time-consuming process, typically involving manual lookup of information by physicians. This process can be expedited by CDS systems that support dynamic retrieval and filtering of adverse drug events (ADE's), but such systems require a source of semantically-coded ADE data. We created a two-component system that addresses this need. First we created a natural language processing application which extracts adverse events from Structured Product Labels and generates a standardized ADE knowledge base. We then built a decision support service that consumes a Continuity of Care Document and returns a list of patient-specific ADE's. Our database currently contains 534,125 ADE's from 5602 product labels. An NLP evaluation of 9529 ADE's showed recall of 93% and precision of 95%. On a trial set of 30 CCD's, the system provided adverse event data for 88% of drugs and returned these results in an average of 620ms.
评估药物潜在不良事件是一个耗时的过程，通常需要医生手动查找信息。支持动态检索和筛选药物不良事件（ADE）的临床决策支持（CDS）系统可以加快这一过程，但此类系统需要语义编码的ADE数据来源。我们创建了一个双组件系统来满足这一需求。首先，我们创建了一个自然语言处理应用程序，该程序从结构化产品标签中提取不良事件并生成标准化的ADE知识库。然后，我们构建了一个决策支持服务，该服务使用连续护理文档并返回特定患者的ADE列表。我们的数据库目前包含来自5602个产品标签的534,125个ADE。对9529个ADE的自然语言处理评估显示召回率为93%，精确率为95%。在30个连续护理文档的试验集上，该系统为88%的药物提供了不良事件数据，平均在620毫秒内返回这些结果。
