The safety of medical products continues to be a significant health concern worldwide. Spontaneous reporting systems (SRS) and pharmacovigilance databases are essential tools for postmarketing surveillance of medical products. Various SRS are employed globally, such as the Food and Drug Administration Adverse Event Reporting System (FAERS), EudraVigilance, and VigiBase. In the pharmacovigilance literature, numerous methods have been proposed to assess product - adverse event pairs for potential signals. In this paper, we introduce an R and Python package that implements a novel pattern discovery method for postmarketing adverse event identification, named Modified Detecting Deviating Cells (MDDC). The package also includes a data generation function that considers adverse events as groups, as well as additional utility functions. We illustrate the usage of the package through the analysis of real datasets derived from the FAERS database.

翻译：医疗产品的安全性始终是全球范围内的重要健康关切。自发报告系统（SRS）与药物警戒数据库是医疗产品上市后监测的关键工具。全球范围内采用多种自发报告系统，例如美国食品药品监督管理局不良事件报告系统（FAERS）、欧盟药物警戒系统（EudraVigilance）以及世界卫生组织全球个例安全报告数据库（VigiBase）。在药物警戒文献中，已有多种方法被提出用于评估药品-不良事件对之间的潜在信号。本文介绍了一个R与Python软件包，其实现了一种用于上市后不良事件识别的新型模式发现方法——改进型偏离单元检测法（MDDC）。该软件包还包含一个将不良事件视为分组的数据生成函数以及其他实用功能函数。我们通过分析源自FAERS数据库的真实数据集，演示了该软件包的使用方法。