Accuracy of an automated knowledge base for identifying drug adverse reactions.
Accuracy of an automated knowledge base for identifying drug adverse reactions.
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DOI：
10.1016/j.jbi.2016.12.005
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发表时间：
2017-02
影响因子：
4.5
通讯作者：
Schuemie MJ
中科院分区：
文献类型：
--
作者：
Voss EA;Boyce RD;Ryan PB;van der Lei J;Rijnbeek PR;Schuemie MJ
药物安全研究人员试图了解特定药物与药物不良反应相关的确定性程度。药物警戒中用于识别、评估和传播医疗产品安全证据的信息来源有多种，包括自发报告、已发表的同行评审文献和产品标签。使用这些证据源进行自动数据处理和分类可以大大减少目前开发用于药物安全研究的阳性和阴性对照参考集（即导致药物不良事件和不导致药物不良事件的药物）所需的手动管理。在本文中，我们探索了一种将不同的信息源自动聚合到单个存储库中的方法，开发了一个预测模型来对药物不良事件关系进行分类，并将这些预测应用于识别统计方法校准的阴性对照的现实世界问题。我们的结果显示，结合所有可用证据的模型具有很高的预测准确性，当对三个手动生成的已知与药物不良事件相关或不相关的药物和病症列表进行测试时，受试者操作曲线下面积≥0.92。该方法的试点实施结果表明，开发一种可扩展的替代方案是可行的，以替代先前用于开发用于药物安全研究的阳性和阴性对照参考集的时间和资源密集型手动管理工作。
Drug safety researchers seek to know the degree of certainty with which a particular drug is associated with an adverse drug reaction. There are different sources of information used in pharmacovigilance to identify, evaluate, and disseminate medical product safety evidence including spontaneous reports, published peer-reviewed literature, and product labels. Automated data processing and classification using these evidence sources can greatly reduce the manual curation currently required to develop reference sets of positive and negative controls (i.e. drugs that cause adverse drug events and those that do not) to be used in drug safety research. In this paper we explore a method for automatically aggregating disparate sources of information together into a single repository, developing a predictive model to classify drug-adverse event relationships, and applying those predictions to a real world problem of identifying negative controls for statistical method calibration. Our results showed high predictive accuracy for the models combining all available evidence, with an area under the receiver-operator curve of ≥ 0.92 when tested on three manually generated lists of drugs and conditions that are known to either have or not have an association with an adverse drug event. Results from a pilot implementation of the method suggests that it is feasible to develop a scalable alternative to the time-and-resource-intensive, manual curation exercise previously applied to develop reference sets of positive and negative controls to be used in drug safety research.