Wang Liwei, Jiang Guoqian, Li Dingcheng, Liu Hongfang
School of Public Health, Jilin University, Changchun, Jilin, China.
Stud Health Technol Inform. 2013;192:1101.
Normalizing data in the Adverse Event Reporting System (AERS), an FDA database, would improve the mining capacity of AERS for drug safety signal detection. In this study, we aim to normalize AERS and build a publicly available normalized Adverse drug events (ADE) data source.he drug information in AERS is normalized to RxNorm, a standard terminology source for medication. Drug class information is then obtained from the National Drug File - Reference Terminology (NDF-RT). Adverse drug events (ADE) are aggregated through mapping with the PT (Preferred Term) and SOC (System Organ Class) codes of MedDRA. Our study yields an aggregated knowledge-enhanced AERS data mining set (AERS-DM). The AERS-DM could provide more perspectives to mine AERS database for drug safety signal detection and could be used by research community in the data mining field.
对美国食品药品监督管理局(FDA)的不良事件报告系统(AERS)中的数据进行标准化处理,将提高AERS挖掘药物安全信号的能力。在本研究中,我们旨在对AERS进行标准化,并建立一个可供公众使用的标准化药品不良事件(ADE)数据源。AERS中的药物信息被标准化为RxNorm,这是一个药物的标准术语来源。然后从国家药品文件-参考术语(NDF-RT)中获取药物类别信息。通过与MedDRA的PT(首选术语)和SOC(系统器官分类)代码进行映射,汇总药品不良事件(ADE)。我们的研究产生了一个汇总的知识增强型AERS数据挖掘集(AERS-DM)。AERS-DM可以为挖掘AERS数据库以检测药物安全信号提供更多视角,并可供数据挖掘领域的研究团体使用。