用知识图谱增强医学术语，自动识别临床试验中的安全信号
Knowledge-based Graphical Method for Safety Signal Detection in Clinical Trials
- 构建Safeterm知识层，将不良事件术语映射到二维语义地图
- 通过收缩率比计算药物特异性偏倚指标，聚类后得贝叶斯几何均值
- 可视化结果可快速定位潜在安全风险，适合药企与监管机构使用
本文提出一种基于知识的图形化方法，用于审查临床试验中的治疗相关不良事件（AE）。该方法在MedDRA基础上引入隐藏的医学知识层Safeterm，通过二维地图捕捉术语间的语义关系。利用此知识层，可自动将不良事件首选术语聚类为相似组，并量化其与试验疾病的相关性。Safeterm地图已在线公开，连接了来自ClinicalTrials.gov的汇总不良事件发生率表。信号检测采用收缩发病率比计算药物特异性偏倚指标，再通过精度加权聚合生成聚类级的贝叶斯几何均值（EBGM）。提供两种可视化输出：显示不良事件发生率的语义地图，以及预期性-偏倚度散点图，支持快速信号识别。在三项历史试验中的应用表明，该自动化方法能清晰复现所有已知安全信号。整体上，通过在MedDRA中加入医学知识层，显著提升了临床试验中不良事件解读的清晰度、效率与准确性。
原文摘要 · Abstract (English)
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.
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