Grigoriev Igor, zu Castell Wolfgang, Tsvetkov Philipp, Antonov Alexey V
Helmholtz Zentrum München GmbH, Department of Scientific Computing, Neuherberg, Germany.
Pharmacoepidemiol Drug Saf. 2014 Aug;23(8):795-801. doi: 10.1002/pds.3561. Epub 2014 Feb 12.
Exploration of the Adverse Event Reporting System (AERS) data by a wide scientific community is limited due to several factors. First, AERS data must be intensively preprocessed to be converted into analyzable format. Second, application of the currently accepted disproportional reporting measures results in false positive signals.
We proposed a data mining strategy to improve hypothesis generation with respect to potential associations.
By numerous examples, we illustrate that our strategy controls the false positive signals. We implemented a free online tool, AERS spider (www.chemoprofiling.org/AERS).
We believe that AERS spider would be a valuable tool for drug safety experts.
由于多种因素,广大科学界对不良事件报告系统(AERS)数据的探索受到限制。首先,AERS数据必须经过深入预处理才能转换为可分析的格式。其次,应用目前公认的不成比例报告措施会产生假阳性信号。
我们提出了一种数据挖掘策略,以改善关于潜在关联的假设生成。
通过大量实例,我们表明我们的策略可控制假阳性信号。我们开发了一个免费的在线工具,即AERS蜘蛛(www.chemoprofiling.org/AERS)。
我们认为AERS蜘蛛对药物安全专家而言将是一个有价值的工具。