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

纳入不良事件和疫苗的多层次本体 用于疫苗安全监测 项目总结 疫苗面临着比大多数药品更严格的安全标准，因为它们是 给健康的人，通常是儿童。疫苗接种后不良事件的有效和严谨分析 (AES)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)是一项全国性的疫苗安全监测计划，其中包含来自 1990年至今。在VAERS上使用了统计方法来提取重要信号 隐藏在这个大型、复杂的数据库中，并提供安全特性的无假设视图 基础数据。然而，现有的方法可能会由于建模而错过对严重不良事件的检测 在不同类型企业之间相互独立的错误假设下。 针对PA-18-873的FOA，本提案涉及具体目标： “创建/评价用于分析疫苗安全性数据的统计方法，包括数据 可从现有数据源获得，如被动报告系统或医疗保健数据库。 我们建议开发一系列疫苗安全监测方法，同时纳入 不良事件本体和疫苗本体。具体地说，我们将使用医学词典 对于监管活动(MedDRA)和疫苗本体(VO)，以形成我们模型的基础 系统地挖掘和监测安全信号。据我们所知，这是第一次 尝试将声发射和疫苗本体直接结合到信号检测方法中。多个企业实体 个体可能是罕见的，不会被发现，但如果它们是相关的，他们可以借力 以增加被标记的机会。此外，借贷实力还会导致 相关AEs的收缩，从而也减少了引起头条新闻的假阳性。另外， 多个AE可能共同指向潜在的不利原因，再加上其他专家 从疫苗本体论的知识，比如疫苗的组件，我们就能够理解 不同类型AEs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