Adverse drug events (ADEs) may occur and lead to severe consequences for the public, even though clinical trials are conducted in the stage of pre-market. Computational methods are still needed to fulfil the task of pharmacosurveillance. In post-market surveillance, the spontaneous reporting system (SRS) has been widely used to detect suspicious associations between medicines and ADEs. However, the passive mechanism of SRS leads to the hysteresis in ADE detection by SRS based methods, not mentioning the acknowledged problem of under-reporting and duplicate reporting in SRS. Therefore, there is a growing demand for other complementary methods utilising different types of healthcare data to assist with global pharmacosurveillance. Among those data sources, prescription data is of proved usefulness for pharmacosurveillance. However, few works have used prescription data for signalling ADEs. In this paper, we propose a data-driven method to discover medicines that are responsible for a given ADE purely from prescription data. Our method uses a logistic regression model to evaluate the associations between up to hundreds of suspected medicines and an ADE spontaneously and selects the medicines possessing the most significant associations via Lasso regularisation. To prepare data for training the logistic regression model, we adapt the design of the case-crossover study to construct case time and control time windows for the extraction of medicine use information. While the case time window can be readily determined, we propose several criteria to select the suitable control time windows providing the maximum power of comparisons. In order to address confounding situations, we have considered diverse factors in medicine utilisation in terms of the temporal effect of medicine and the frequency of prescription, as well as the individual effect of patients on the occurrence of an ADE. To assess the performance of the proposed method, we conducted a case study with a real-world prescription dataset. Validated by the existing domain knowledge, our method successfully traced a wide range of medicines that are potentially responsible for the ADE. Further experiments were also carried out according to a recognised gold standard, our method achieved a sensitivity of 65.9% and specificity of 96.2%.

即使临床试验是在上市前阶段进行的，也可能发生药物不良事件（ADE）并给公众带来严重后果。仍然需要计算方法来完成药物监测的任务。在上市后监测中，自发报告系统 (SRS) 已被广泛用于检测药物与 ADE 之间的可疑关联。然而，SRS 的被动机制导致基于 SRS 的方法检测 ADE 的滞后，更不用说 SRS 中公认的漏报和重复报告的问题。因此，对利用不同类型的医疗保健数据来协助全球药物监测的其他补充方法的需求不断增长。在这些数据源中，处方数据被证明可用于药物监测。然而，很少有作品使用处方数据来发送 ADE。在本文中，我们提出了一种数据驱动的方法，可以纯粹从处方数据中发现导致给定 ADE 的药物。我们的方法使用逻辑回归模型来自发评估多达数百种可疑药物与 ADE 之间的关联，并通过套索正则化选择具有最显着关联的药物。为了准备用于训练逻辑回归模型的数据，我们调整了病例交叉研究的设计，以构建病例时间和控制时间窗口，以提取药物使用信息。虽然可以很容易地确定案例时间窗口，但我们提出了几个标准来选择合适的控制时间窗口，以提供最大的比较能力。为了解决混乱的情况，我们考虑了药物使用的不同因素，包括药物的时间效应和处方频率，以及患者对 ADE 发生的个体影响。为了评估所提出方法的性能，我们使用真实世界的处方数据集进行了案例研究。通过现有领域知识的验证，我们的方法成功地追踪了可能导致 ADE 的各种药物。还根据公认的金标准进行了进一步的实验，我们的方法实现了 65.9% 的灵敏度和 96.2% 的特异性。为了评估所提出方法的性能，我们使用真实世界的处方数据集进行了案例研究。通过现有领域知识的验证，我们的方法成功地追踪了可能导致 ADE 的各种药物。还根据公认的金标准进行了进一步的实验，我们的方法实现了 65.9% 的灵敏度和 96.2% 的特异性。为了评估所提出方法的性能，我们使用真实世界的处方数据集进行了案例研究。通过现有领域知识的验证，我们的方法成功地追踪了可能导致 ADE 的各种药物。还根据公认的金标准进行了进一步的实验，我们的方法实现了 65.9% 的灵敏度和 96.2% 的特异性。