Leveraging artificial intelligence methods and electronic health records for pediatric pharmacovigilance

利用人工智能方法和电子健康记录进行儿科药物警戒

• 批准号: 10750074
• 负责人: Cosmin Adrian Bejan
• 金额: $ 48.13万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10750074
• 关键词: Academic Medical Centers; Address; Adolescent; Adult; Adverse event; Adverse reactions; Affect; Area; Behavior; Characteristics; Child; Childhood; Clinical; Clinical Decision Support Systems; Clinical Trials; Code; Congenital Abnormality; Data; Data Sources; Database Management Systems; Databases; Development; Drug Combinations; Drug Exposure; Drug Interactions; Drug usage; Early Diagnosis; Effectiveness; Electronic Health Record; Ethics; Exclusion; Funding; Future; Goals; Health Insurance; Healthcare; Infant; Knowledge; Machine Learning; Marketing; Medicaid; Methodology; Methods; Monitor; Mothers; Natural Language Processing; Newborn Infant; Outcome; Patients; Performance; Pharmaceutical Preparations; Pharmacoepidemiology; Physiological; Population; Pregnancy; Pregnant Women; Real-Time Systems; Regulation; Reporting; Research; Risk; Safety; Serious Adverse Event; Structure; Subgroup; System; Teratogenic effects; Testing; Text; Thalidomide; Time; Toxic effect; Translating; Translational Research; United States Food and Drug Administration; Update; Woman; Work; World Health; absorption; adverse drug reaction; artificial intelligence method; clinical decision support; clinical practice; deep learning; drug development; drug testing; electronic health record system; electronic structure; health determinants; improved; insurance claims; off-label use; pediatric patients; pharmacovigilance; phenome; post-market; pregnant; prevent; recruit; reproductive; research study; safety assessment; secondary analysis; side effect; substance use; tool; translational potential; translational study; trend; trial design

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.

项目摘要 美国食品和药物管理局的一个总体目标是有效实施后市场 已经批准的药物的药物警戒能力。实现这一目标的小儿人口是 特别是挑战。例如，目前对安全性，风险，毒品互动以及 由于对严格的法规施加的严格法规 孕妇参与药物开发试验。此外，许多药物的安全性和效率 由于缺乏对儿童的临床试验，因此缺乏儿科使用。因此，小儿练习经常 涉及使用具有未知副作用的药物的“非标签”使用。这可能会在 儿科患者，包括严重不良药物反应和毒性，可能影响其发育和 未来的生殖能力。大量实际医疗保健数据（例如电子）的可用性 健康记录（EHRS）提供了满足有效调查效果的关键需求的机会 大规模对小儿种群的药物暴露。我们的目标是进行毒品和现象 关于大型EHR母子二元数据库的协会研究，该数据库将使我们能够学习不良的小儿 与1）在怀孕期间和妊娠之前和药物的使用相关的结果；和 2）儿童在所有发展里程碑中的药物暴露。次要分析将包括 母亲的药物使用暴露与小儿结局之间的关联，药物相互作用范围 协会研究和药物 - 固定性使用相互作用广泛的关联研究。此外，我们将利用 人工智能方法，例如自然语言处理（NLP）和机器学习以解决 暴露分类错误并改善了拟议研究的小儿结果鉴定。我们的项目 目的是：1）进行高通量药物ePIDEPIDEMIologic研究以识别不良的小儿结局， 2）使用利益相关者评估实时儿科药物守护系统的临床实用性 参与策略。该提案的预期结果是一种了解利益相关者的工具来监视 儿童实时的药物反应不良。这将为临床部署铺平道路 决策支持系统，用于早期检测小儿种群中不良药物反应和实时的药物反应系统 鉴定有这种负面结果风险的患者。