Incorporation of multilevel ontologies of adverse events and vaccines for vaccine safety surveillance

不良事件和疫苗的多级本体论的结合 用于疫苗安全监测 项目概要 疫苗面临着比大多数药品更严格的安全标准，因为它们是给予的 对于健康的人，通常是儿童。对疫苗接种后不良事件进行有效和严格的分析 （AE）对于确保疫苗的安全性至关重要。疫苗不良事件报告系统 (VAERS) 是一项国家疫苗安全监测计划，其中包含来自以下机构的自发报告： 1990年至今。 VAERS 已使用统计方法来提取重要信号 隐藏在这个庞大而复杂的数据库中，并提供了安全特性的无假设视图 基础数据。然而，由于建模，现有方法可能会错过检测严重的 AE 不同类型 AE 之间独立性的错误假设。 为了响应 FOA，PA-18-873，该提案解决了具体目标： “创建/评估用于分析疫苗安全性数据的统计方法，包括数据 可从现有数据源（例如被动报告系统或医疗保健数据库）获取。” 我们建议开发一系列疫苗安全监测方法，同时结合 不良事件本体论以及疫苗本体论。具体来说，我们将使用医学词典 监管活动 (MedDRA) 和疫苗本体 (VO) 构成我们模型的基础 系统地挖掘和监控安全信号。据我们所知，这是第一个 尝试将AE和疫苗本体直接纳入信号检测方法中。多个 AE 个别情况可能很罕见，无法被发现，但如果它们相关，它们就可以借用力量 彼此之间增加被标记的机会。此外，借贷强度导致 相关 AE 的减少，从而也减少了引人注目的误报。另外， 多个 AE 可能共同指向潜在的不良原因，再加上额外的专家 从疫苗本体的知识，比如疫苗的成分，我们就能了解 不同类型 AE 的根本原因。 1

Incorporation of multilevel ontologies of adverse events and vaccines for vaccine safety surveillance PROJECT SUMMARY Vaccines face tougher safety standards than most pharmaceutical products because they are given to healthy people, often children. Effective and rigorous analyses of post-vaccination adverse events (AEs) is critical to ensure the safety of vaccines. The Vaccine Adverse Event Reporting System (VAERS) is a national vaccine safety surveillance program which contains spontaneous reports from 1990 to present. Statistical approaches have been used on VAERS to extract important signals hidden in this large, complex database and offer a hypothesis-free view of the safety characteristics in the underlying data. However, existing methods may miss detecting serious AEs due to modeling under the false assumption of independence between different types of AEs. In response to the FOA, PA-18-873, this proposal addresses the specific objective: “creation/evaluation of statistical methodologies for analyzing data on vaccine safety, including data available from existing data sources such as passive reporting systems or healthcare databases.” We propose to develop a series of methods for vaccine safety surveillance while incorporating adverse event ontology as well as vaccine ontology. Specifically, we will use the Medical Dictionary for Regulatory Activities (MedDRA) and the vaccine ontology (VO) to form the basis of our models for systematically mining and monitoring safety signals. To the best of our knowledge, this is the first attempt to directly incorporate AE and vaccine ontologies in the signal detection method. Multiple AEs may individually be rare enough to go undetected, but if they are related, they can borrow strength from each other to increase the chance of being flagged. Furthermore, borrowing strength induces shrinkage of related AEs, thereby also reducing headline-grabbing false positives. Additionally, multiple AEs may collectively point to an underlying adverse cause, combined with additional expert knowledge from the vaccine ontology, such as vaccine components, we will be able to understand the root cause of different types of AEs. 1