将临床试验数据标准化，实现跨试验药物安全分析
CTG-DB: An Ontology-Based Transformation of ClinicalTrials.gov to Enable Cross-Trial Drug Safety Analyses
- 构建CTG-DB数据库，用MedDRA标准术语统一原始不良事件记录
- 保留各试验组人数信息，支持与安慰剂组对比的安全性分析
- 适合药监机构和制药公司做大规模药物安全性信号检测
ClinicalTrials.gov（CT.gov）是全球最大的公开临床研究注册库，但其以注册为中心的架构和不良事件（AE）术语不一致，限制了系统性药物警戒（PV）分析。不良事件通常以研究人员报告的文本形式记录，缺乏标准化标识符，需人工整理才能识别一致的安全性概念。本文提出临床试验数据库转换框架（CTG-DB），一个开源处理流程，可摄入完整的CT.gov XML档案，生成基于医学监管活动词典（MedDRA）标准化术语的关系型数据库。该数据库保留各试验组的受试者人数，明确表示安慰剂组和对照组，并通过确定性精确匹配与模糊匹配方法实现不良事件术语的规范化，确保映射过程透明可复现。该框架支持概念级检索与跨试验聚合，实现可扩展的以安慰剂为参照的安全性分析，促进临床试验证据在下游药物警戒信号检测中的集成。
原文摘要 · Abstract (English)
ClinicalTrials .gov (CT .gov) is the largest publicly accessible registry of clinical studies, yet its registry-oriented architecture and heterogeneous adverse event (AE) terminology limit systematic pharmacovigilance (PV) analytics. AEs are typically recorded as investigator-reported text rather than standardized identifiers, requiring manual reconciliation to identify coherent safety concepts. We present the ClinicalTrials .gov Transformation Database (CTG-DB), an open-source pipeline that ingests the complete CT .gov XML archive and produces a relational database aligned to standardized AE terminology using the Medical Dictionary for Regulatory Activities (MedDRA). CTG-DB preserves arm-level denominators, represents placebo and comparator arms, and normalizes AE terminology using deterministic exact and fuzzy matching to ensure transparent and reproducible mappings. This framework enables concept-level retrieval and cross-trial aggregation for scalable placebo-referenced safety analyses and integration of clinical trial evidence into downstream PV signal detection.
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