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Generalized enrichment analysis improves the detection of adverse drug events from the biomedical literature

机译:广义富集分析可改善生物医学文献中药物不良事件的检测

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Background 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. Results 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 ion 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 Conclusions 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 ion levels to complement current drug safety methods. We provide an R package for exploration of alternative ion levels of adverse event terms based on information content.
机译:背景技术从生物医学文献中确定上市药品与不良事件之间的关联有助于药品安全监测工作。评估此类文献派生的关联的重要性并确定应捕获它们的粒度仍然是一个挑战。在这里,我们评估了基于信息内容从MeSH定义不良事件术语的选择如何改善药物和药物类别的不良事件的检测。结果我们分析了从MEDLINE的文章索引中提取的105,354种候选药物不良事件对。首先,我们根据提取出的不良事件用语的信息内容,将其汇总成更高级别的MeSH用语,从而将它们统一起来。然后,我们使用条件超几何检验(确定相关术语之间的依存关系进行调整)来确定与药物和药物类别相关的不良事件的统计丰富度。我们将结果与基于不成比例分析(比例报告比率,PRR)的方法进行比较,并使用涵盖174种药物和4种事件的药物不良事件关联的黄金标准,通过我们的广义富集分析(GEA)方法量化信号检测的改善。对于单一药物,在我们所有的四个不良事件结果中,最佳的GEA方法(精度:.92 / Recall:.71 / F1-measure:.80)优于基于PRR的最佳方法(.69 / .69 / .69)。黄金标准。对于药物类别,当增加不良事件用语的离子水平时,我们的GEA表现类似(.85 / .69 / .74)。最后,在检查我们的MEDLINE集中对应于ATC中化学物质的1609种药物后,我们发现在应用pGEA的情况下有1379种药物(10,122个独特的不良事件关联)的信号。结论我们提出了一种基于广义富集分析的方法,可以用于检测给定粒度级别的药物,药物类别和不良事件之间的关联,同时更正事件之间的已知依赖性。我们的研究证明了GEA的使用,以及选择合适的离子水平来补充当前药物安全性方法的重要性。我们提供了一个R包,用于根据信息内容探索不良事件项的替代离子水平。

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