We present a graphical, knowledge-based method for reviewing treatment-emergent adverse events (AEs) in clinical trials. The approach enhances MedDRA by adding a hidden medical knowledge layer (Safeterm) that captures semantic relationships between terms in a 2-D map. Using this layer, AE Preferred Terms can be regrouped automatically into similarity clusters, and their association to the trial disease may be quantified. The Safeterm map is available online and connected to aggregated AE incidence tables from ClinicalTrials.gov. For signal detection, we compute treatment-specific disproportionality metrics using shrinkage incidence ratios. Cluster-level EBGM values are then derived through precision-weighted aggregation. Two visual outputs support interpretation: a semantic map showing AE incidence and an expectedness-versus-disproportionality plot for rapid signal detection. Applied to three legacy trials, the automated method clearly recovers all expected safety signals. Overall, augmenting MedDRA with a medical knowledge layer improves clarity, efficiency, and accuracy in AE interpretation for clinical trials.

翻译：我们提出了一种图形化、基于知识的方法，用于审查临床试验中治疗引发的不良事件（AEs）。该方法通过添加一个隐藏的医学知识层（Safeterm）来增强MedDRA，该层在二维图中捕捉术语之间的语义关系。利用这一层，AE首选术语可以自动重新分组为相似性簇，并且它们与试验疾病的关联性可以被量化。Safeterm图可在线上获取，并与来自ClinicalTrials.gov的汇总AE发生率表相连接。对于信号检测，我们使用收缩发生率比计算治疗特异性的不成比例性指标。随后通过精度加权聚合推导出簇级的EBGM值。两种可视化输出支持解释：显示AE发生率的语义图以及用于快速信号检测的预期性与不成比例性关系图。应用于三项历史试验时，该自动化方法清晰地恢复了所有预期的安全性信号。总体而言，通过医学知识层增强MedDRA，提高了临床试验中AE解释的清晰度、效率和准确性。