AERS蜘蛛：一种用于挖掘不良事件报告系统中统计关联的在线交互式工具。
AERS spider: an online interactive tool to mine statistical associations in Adverse Event Reporting System.
作者信息
Grigoriev Igor, zu Castell Wolfgang, Tsvetkov Philipp, Antonov Alexey V
机构信息
Helmholtz Zentrum München GmbH, Department of Scientific Computing, Neuherberg, Germany.
出版信息
Pharmacoepidemiol Drug Saf. 2014 Aug;23(8):795-801. doi: 10.1002/pds.3561. Epub 2014 Feb 12.
BACKGROUND
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.
METHODS
We proposed a data mining strategy to improve hypothesis generation with respect to potential associations.
RESULTS
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).
CONCLUSIONS
We believe that AERS spider would be a valuable tool for drug safety experts.
背景
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。
方法
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。
结果
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）。
结论
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。
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