Leveraging artificial intelligence methods and electronic health records for pediatric pharmacovigilance

项目总结 美国食品和药物管理局的首要目标之一是有效地实施上市后 已经批准的药物的药物警戒能力。在儿科人群中实现这一目标是 特别具有挑战性。例如，目前对这种药物的安全性、风险、药物相互作用以及 怀孕期间使用的许多药物的致畸作用，因为对 孕妇参与药物开发试验。此外，许多药物治疗的安全性和有效性 由于缺乏对儿童的临床试验，儿科使用很少。出于这个原因，儿科医生经常 涉及“标签外”使用副作用未知的药物。这可能会导致不可预测的悲剧性影响 儿科患者包括严重的药物不良反应和毒性，可影响其发育和 未来的生殖能力。大量真实医疗保健数据的可用性，如电子 健康记录(EHR)提供了一个机会，以满足有效调查以下影响的关键需求 儿童人群中大规模的药物暴露。我们的目标是在整个药物和现象范围内 对大型EHR母子二联体数据库的关联研究，这将使我们能够研究儿童不良反应 与1)母亲在怀孕期间和怀孕前接触药物和物质有关的结果；以及 2)儿童在整个发育阶段的药物暴露情况。二次分析将包括 母亲的药物使用暴露与儿科结局、药物-药物相互作用范围之间的关系 关联性研究和药物与物质使用相互作用的广泛关联性研究。此外，我们将利用 自然语言处理(NLP)和机器学习等人工智能方法 暴露于错误分类和改善儿科结果识别的拟议研究。我们的项目 目的是：1)进行高通量药物流行病学研究，以确定儿科的不良后果， 2)使用利益相关者评估实时儿科药物警示系统的临床实用性 参与战略。这项提案的预期结果是一个利益相关者知情的监督工具 实时监测儿童药品不良反应情况。这将为临床部署铺平道路 儿科人群药物不良反应早期检测与实时决策支持系统 确定有这种负面结果风险的患者。

PROJECT SUMMARY One overarching goal of the US Food and Drug Administration is to effectively implement post-market pharmacovigilance capabilities of already approved medications. Achieving this goal for pediatric population is particularly challenging. For example, little is currently known about the safety, risks, drug-interactions, and teratogenic effects of many drugs used during pregnancy due to the strict regulations imposed for the participation of pregnant women in drug development trials. Further, safety and efficacy of many drugs for pediatric use is scarce due to the lack of clinical trials on children. For this reason, pediatric practice often involves “off-label” use of drugs with unknown side effects. This may cause unpredictable and tragic effects in pediatric patients including severe adverse drug reactions and toxicity that can affect their development and future reproductive capacity. The availability of large volumes of real-world healthcare data such as electronic health records (EHRs) provides an opportunity to meet the critical need of effectively investigating the effect of drug exposures on pediatric populations at large scale. Our goal is to conduct drug- and phenome-wide association studies on a large EHR database of mother-child dyads that will allow us to study adverse pediatric outcomes associated with 1) drug and substance use exposures of mothers during and before pregnancy; and 2) drug exposures of children during all their developmental milestones. Secondary analyses will include associations between substance use exposure of mothers and pediatric outcomes, drug-drug interaction wide association studies, and drug-substance use interaction wide association studies. Further, we will leverage artificial intelligence methods such as natural language processing (NLP) and machine learning to address exposure misclassification and improve pediatric outcome identification for the proposed studies. Our project aims are to: 1) conduct high-throughput pharmacoepidemiologic studies to identify adverse pediatric outcomes, and 2) evaluate the clinical utility of a real-time pediatric pharmacovigilance system using stakeholder engagement strategies. The expected outcome of this proposal is a stakeholder-informed tool to monitor adverse drug reactions of children in real-time. This will pave the way towards the deployment of a clinical decision support system for early detection of adverse drug reactions in pediatric populations and for real-time identification of patients who are at risk of such negative outcomes.