Elkin Peter L, Carter John S, Nabar Manasi, Tuttle Mark, Lincoln Michael, Brown Steven H
Mount Sinai School of Medicine.
Stud Health Technol Inform. 2011;166:38-47.
The majority of questions that arise in the practice of medicine relate to drug information. Additionally, adverse reactions account for as many as 98,000 deaths per year in the United States. Adverse drug reactions account for a significant portion of those errors. Many authors believe that clinical decision support associated with computerized physician order entry has the potential to decrease this adverse drug event rate. This decision support requires knowledge to drive the process. One important and rich source of drug knowledge is the DailyMed product labels. In this project we used computationally extracted SNOMED CT™ codified data associated with each section of each product label as input to a rules engine that created computable assertional knowledge in the form of semantic triples. These are expressed in the form of "Drug" HasIndication "SNOMED CT™". The information density of drug labels is deep, broad and quite substantial. By providing a computable form of this information content from drug labels we make these important axioms (facts) more accessible to computer programs designed to support improved care.
医学实践中出现的大多数问题都与药物信息有关。此外，在美国，不良反应每年导致多达98000人死亡。药物不良反应在这些错误中占很大一部分。许多作者认为，与计算机化医生医嘱录入相关的临床决策支持有可能降低这种药物不良事件发生率。这种决策支持需要知识来推动这一过程。一个重要且丰富的药物知识来源是每日医学产品标签。在这个项目中，我们使用通过计算提取的与每个产品标签各部分相关的SNOMED CT™编码数据，作为规则引擎的输入，该规则引擎以语义三元组的形式创建可计算的断言性知识。这些知识以“药物”有适应症“SNOMED CT™”的形式表达。药物标签的信息密度深、广且相当可观。通过提供药物标签中这些信息内容的可计算形式，我们使这些重要的公理（事实）更容易被旨在支持改善医疗护理的计算机程序获取。
