Signal Detection of Drug Complications Applying Association Rule Learning for Stevens-Johnson Syndrome
Signal Detection of Drug Complications Applying Association Rule Learning for Stevens-Johnson Syndrome
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DOI：
10.2751/jcac.10.118
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发表时间：
2009-01-01
影响因子：
--
通讯作者：
Takagi, Tatsuya
中科院分区：
文献类型：
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作者：
Shirakuni, Yuko;Okamoto, Kousuke;Takagi, Tatsuya
当一个病人同时服用两种或两种以上的药物时，药物引起的不良事件变得复杂。我们选择严重的皮肤表现之一的“Stevens-Johnson综合征”作为研究对象。数据源是由美国食品药品监督管理局（FDA）构建的数据库。FDA的上市后安全性监测计划得到了不良事件报告系统（AERS）的支持。AERS设计了一个计算机化的信息数据库。为了分析本研究中合并用药与SJS之间的关系，我们应用了关联规则学习。我们的目的是提出一个有效的程序，使检测信号的药物相关的不良事件，而不假设的参与特定的药物。我们定义了新的K值，用于评估现有的信号检测。根据K值准则对关联规则进行评价。因此，建议通过合并两种伴随药物获得强信号。本研究中的关联规则学习适用于分析不良事件与药物对之间的关系。
The adverse events induced by drugs have been complicated, when two or more drugs are administrated for a patient. We selected "Stevens-Johnson Syndrome (SJS)" as a research object, which is one of the severe skin manifestations. The data source is a database constructed by the Food and Drug Administration (FDA). FDA's post-marketing safety surveillance program is supported by the Adverse Event Reporting System (AERS). AERS is designed with a computerized information database. To analyze the relationships between the concurrent medication and SJS in this study, we applied association rule learning. Our purpose is to propose an efficient procedure that enables the detection of signals for drugs related to an adverse event, without assuming the involvement of a specific drug. We defined new value K for the evaluation of existing signal detection. Association rule was evaluated according to criterion K value. As a result, it was suggested to obtain a strong signal by combining two concomitant drugs. Association rule learning in this study was applicable for the analysis of the relationships between adverse events and pairs of drugs.