Pharmacovigilance Methods: Leveraging Heterogeneous Adverse Drug Reaction Data

药品不良反应(ADR)是患者和医疗保健的主要负担，造成可预防的 住院和死亡，并招致巨大的费用。这项建议的长期目标是推进 通过使用自动化方法发现新的严重不良反应，从而确保患者安全并降低成本 结合大量和不同的患者群体以及文献中的信息。曾经有过 在药物警戒方面取得了相当大的进展，但还需要做更多的工作。例如万络，一种常用的 最近发现，这种药物至少会导致8.8万例心肌梗死，突显出 现有方法的不足。到目前为止，方法主要依赖于单一数据源的使用， 主要来自联邦食品和药物管理局不良事件报告系统(FAERS)和 电子健康记录(EHR)。虽然很重要，但每个来源都有不同的限制和 优势，因此，将这些数据结合起来应该会产生更有效的药物安全性 通过增加统计能力以及允许每个数据源相互补充来进行监视 消息来源。我们已经开发了与每个单一来源相关联的方法，因此，这 是一个极好的机会，可以在我们的研究成果的基础上，推动 药物警戒。 更具体地说，我们将a)从电子健康中获取并整合全面的临床数据 利用自然语言为不同人群服务的两个不同卫生保健站点的记录(EHR) 处理(NLP)以获得海量的细粒度数据，然后通过开发数据挖掘 临床数据方法学以检测新的ADR信号，b)分析与治疗相关的风险差异 两个EHR人群之间的因素，如种族和民族差异，c)在 使用已建立的方法建立FAERS数据库，d)制定获取ADR信号的改进方法 基于文献中的信息，以及e)开发利用上述来源的结果的方法 最大限度地提高效率。我们将重点关注8个严重的ADR，并收集高质量的参考标准 这些ADR，以便我们能够评估和比较不同检测方法的性能 单独的以及组合来源的方法。 这项提议很好地克服了与现有自动化方法相关的问题，这些方法 主要基于对个别数据源的使用。我们相信这些方法将是有效的。 因为我们有强大的基础设施可供我们建设。最重要的是，在 此建议提供了一个利用异类数据源显著改进 保障患者安全，降低成本。

Adverse drug reactions (ADRs) are a major burden for patients and healthcare, causing preventable hospitalizations and deaths, and incurring a huge cost. The long-term objective of this proposal is to advance patient safety and reduce costs by discovering novel serious ADRs through use of automated methods that combine information from large and varied patient populations as well as from the literature. There have been considerable advances in pharmacovigilance, but more work is needed. For example, Vioxx, a commonly used drug, was recently found to cause at least 88,000 occurrences of myocardial infarction, highlighting the insufficiency of current methods. To date, methods have mainly depended on the use of single sources of data, primarily from the Federal Food and Drug Administration Adverse Event Reporting System (FAERS) and from electronic health records (EHRS). Although important, each of the sources has different limitations and advantages, and therefore, combining the data across them should lead to more effective drug safety surveillance by increasing the statistical power, and also by allowing each data source to complement the other sources. We already have developed methods associated with each of the single sources, and therefore, this is an excellent opportunity to build upon our research accomplishments to advance the state of the art in pharmacovigilance. More specifically, we will a) acquire and combine comprehensive clinical data from the electronic health records (EHRs) of two different health care sites serving diverse populations by utilizing natural language processing (NLP) to obtain vast quantities of fine-grained data, and then by developing data mining methodologies on the clinical data to detect novel ADR signals, b) analyze differences in therapy-related risk factors between the two EHR populations, such as racial and ethnic differences, c) detect ADR signals in the FAERS database using an established methodology, d) develop improved methods to acquire ADR signals based on information in the literature, and e) develop methods that utilize the results from the above sources to maximize effectiveness. We will focus on eight serious ADRs, and collect a high-quality reference standard for those ADRs so that we will be able to evaluate and compare performance of the different detection methods individually as well as the methods that combine the sources. This proposal is well positioned to overcome problems associated with existing automated methods, which are primarily based on use of individual sources of data. We are confident the methods will be effective because a strong infrastructure is in place for us to build upon. Most importantly, the methodology developed in this proposal presents an excellent chance to leverage heterogeneous data sources to dramatically improve patient safety and reduce costs.