Generalized enrichment analysis improves the detection of adverse drug events from the biomedical literature.
Generalized enrichment analysis improves the detection of adverse drug events from the biomedical literature.
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DOI：
10.1186/s12859-016-1080-z
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发表时间：
2016-06-23
影响因子：
3
通讯作者：
Shah NH
中科院分区：
文献类型：
--
作者：
Winnenburg R;Shah NH
从生物医学文献中识别上市药物和不良事件之间的关联有助于药物安全性监测工作。评估这些文献衍生关联的重要性并确定捕获它们的粒度仍然是一个挑战。在这里，我们评估如何定义一个选择的不良事件术语从MeSH，基于信息内容，可以提高药物和药物类的不良事件的检测。我们分析了从MEDLINE文章索引中提取的105，354个候选药物不良事件对。首先，我们协调提取的不良事件的条款聚合成更高级别的MeSH条款的基础上的条款的信息内容。然后，我们使用调整相关术语之间依赖性的条件超几何检验来确定与药物和药物类别相关的不良事件的统计富集。我们比较了我们的结果与方法的基础上的重复性分析（比例报告比，PRR）和量化的改善信号检测与我们的广义富集分析（GEA）的方法使用的黄金标准的药物不良事件协会跨越174种药物和4个事件。对于单一药物，在我们的金标准中，最佳GEA方法（精度：.92/召回：.71/F1-测量：.80）在所有四种不良事件结局上优于基于PRR的最佳方法（.69/.69/.69）。对于药物类别，当增加不良事件术语的抽象水平时，我们的GEA表现相似（0.85/0.69/0.74）。最后，在我们的MEDLINE集中检查了1609种药物，这些药物映射到ATC中的化学物质，我们发现了1379种药物（10，122种独特的不良事件关联）应用GEA的信号，p < 0.005。我们提出了一种基于广义富集分析的方法，该方法可用于在给定的粒度水平上检测药物、药物类别和不良事件之间的关联，同时校正事件之间的已知依赖关系。我们的研究证明了GEA的使用，以及选择适当的提取水平以补充当前药物安全方法的重要性。我们提供了一个R包，用于探索基于信息内容的不良事件术语的替代抽象级别。本文的在线版本（doi：10.1186/s12859-016-1080-z）包含补充材料，可供授权用户使用。
Identification of associations between marketed drugs and adverse events from the biomedical literature assists drug safety monitoring efforts. Assessing the significance of such literature-derived associations and determining the granularity at which they should be captured remains a challenge. Here, we assess how defining a selection of adverse event terms from MeSH, based on information content, can improve the detection of adverse events for drugs and drug classes. We analyze a set of 105,354 candidate drug adverse event pairs extracted from article indexes in MEDLINE. First, we harmonize extracted adverse event terms by aggregating them into higher-level MeSH terms based on the terms’ information content. Then, we determine statistical enrichment of adverse events associated with drug and drug classes using a conditional hypergeometric test that adjusts for dependencies among associated terms. We compare our results with methods based on disproportionality analysis (proportional reporting ratio, PRR) and quantify the improvement in signal detection with our generalized enrichment analysis (GEA) approach using a gold standard of drug-adverse event associations spanning 174 drugs and four events. For single drugs, the best GEA method (Precision: .92/Recall: .71/F1-measure: .80) outperforms the best PRR based method (.69/.69/.69) on all four adverse event outcomes in our gold standard. For drug classes, our GEA performs similarly (.85/.69/.74) when increasing the level of abstraction for adverse event terms. Finally, on examining the 1609 individual drugs in our MEDLINE set, which map to chemical substances in ATC, we find signals for 1379 drugs (10,122 unique adverse event associations) on applying GEA with p < 0.005. We present an approach based on generalized enrichment analysis that can be used to detect associations between drugs, drug classes and adverse events at a given level of granularity, at the same time correcting for known dependencies among events. Our study demonstrates the use of GEA, and the importance of choosing appropriate abstraction levels to complement current drug safety methods. We provide an R package for exploration of alternative abstraction levels of adverse event terms based on information content. The online version of this article (doi:10.1186/s12859-016-1080-z) contains supplementary material, which is available to authorized users.