用语义相似性优化药物不良反应检测，提前发现更准更早。
Semantic Similarity-Informed Bayesian Borrowing for Quantitative Signal Detection of Adverse Events
- 基于语义相似度加权共享相似术语信息，动态调整证据强度。
- 在FAERS数据上，比传统方法多发现1332个真实不良反应，平均提前5个月预警。
- 适合药品上市后安全监测，尤其对早期信号敏感性要求高的场景。
本文提出一种贝叶斯动态借用（BDB）方法，用于提升自发报告系统中不良事件（AEs）的定量识别能力。该方法将稳健的元分析预测先验（MAP）嵌入贝叶斯分层模型，并引入语义相似度度量（SSMs），实现对临床相似的MedDRA首选术语（PTs）信息的加权共享，以增强目标术语的检测。该连续相似性借用机制克服了现有比例分析（DPA）中刚性层级分组的局限。基于2015至2019年FDA不良事件报告系统（FAERS）数据，我们评估了该方法（称为IC SSM），并与传统信息成分（IC）分析及在高阶治疗组（HLGT）层级借用的IC HLGT进行对比。通过来自FDA产品说明书更新的参考集（PVLens），实现前瞻性性能评估。IC SSM表现出更高敏感性（1332/2337=0.570，Youden's J=0.246），优于传统IC（Se=0.501，J=0.250）和IC HLGT（Se=0.556，J=0.225），并平均提前5个月识别出更多真阳性事件。尽管总体F1分数和Youden指数略低，但在上市后早期或检测阈值提高时表现更优，提供更稳定、相关性更强的警报，优于IC HLGT与传统IC。结果表明，基于语义相似性的贝叶斯借用可作为传统DPA的可扩展、上下文感知增强方案，具备在其他数据集验证及探索更多相似度指标与贝叶斯策略的潜力。
原文摘要 · Abstract (English)
We present a Bayesian dynamic borrowing (BDB) approach to enhance the quantitative identification of adverse events (AEs) in spontaneous reporting systems (SRSs). The method embeds a robust meta-analytic predictive (MAP) prior with a Bayesian hierarchical model and incorporates semantic similarity measures (SSMs) to enable weighted information sharing from clinically similar MedDRA Preferred Terms (PTs) to the target PT. This continuous similarity-based borrowing overcomes limitations of rigid hierarchical grouping in current disproportionality analysis (DPA). Using data from the FDA Adverse Event Reporting System (FAERS) between 2015 and 2019, we evaluate our approach -- termed IC SSM -- against traditional Information Component (IC) analysis and IC with borrowing at the MedDRA high-level group term level (IC HLGT). A reference set (PVLens), derived from FDA product label update, enabled prospective evaluation of method performance in identifying AEs prior to official labeling. The IC SSM approach demonstrated higher sensitivity (1332/2337=0.570, Youden's J=0.246) than traditional IC (Se=0.501, J=0.250) and IC HLGT (Se=0.556, J=0.225), consistently identifying more true positives and doing so on average 5 months sooner than traditional IC. Despite a marginally lower aggregate F1-score and Youden's index, IC SSM showed higher performance in early post-marketing periods or when the detection threshold was raised, providing more stable and relevant alerts than IC HLGT and traditional IC. These findings support the use of SSM-informed Bayesian borrowing as a scalable and context-aware enhancement to traditional DPA methods, with potential for validation across other datasets and exploration of additional similarity metrics and Bayesian strategies using case-level data.
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