Bridging the Gap between Genomics and Clinical Outcomes in CHD

项目摘要/摘要 NHLBI已对儿科心脏基因组学联盟(PCGC)进行了广泛投资，认识到 将基因组发现转化为先天性心脏病的优化管理和治疗策略 疾病(CHD)只能在多中心、协作研究的背景下实现。目前，盈科委员会 缺乏两个基本能力，阻碍了其确定冠心病结果的基因组基础的能力：(1) 从电子健康记录中提取相关的、机器可读的临床数据的健壮机制 跨多个机构的(EHR)；以及(2)能够逗乐的强大的人工智能(AI)平台 除了母体因素、表型、基因类型、基因功能和临床之间的复杂相互作用 结果。在这里，我们通过集合内容专家团队，为这些挑战提出创新的解决方案 利用现有基础设施直接从参与的PCGC的EHR中获取相关成果 并通过设计用于成果研究的最佳实践人工智能工具。我们的主要目标是提供愿景， 基础设施和专业知识，以协作方式增强CHD成果研究，促进知识交流， 并培养下一代基因组科学家。我们建议利用现有的数据基础设施来获得 电子健康记录(EHR)和其他临床变量的规模，通过与其他研究网络合作 要创建PCGC数据资源，请执行以下操作。利用这一资源，我们将创建和部署一个人工智能平台 基于智能(AI)的CHD结局研究预测因子，目标是将基因组发现转化为 改善对冠心病的管理和治疗策略。

PROJECT SUMMARY/ABSTRACT The NHLBI has invested extensively in the Pediatric Cardiac Genomics Consortium (PCGC), recognizing that translating genomic discoveries into optimized management and therapeutic strategies for congenital heart disease (CHD) can only be achieved in the context of multi-center, collaborative research. Currently, the PCGC is lacking two fundamental capabilities that hinder its ability to define the genomic basis for CHD outcomes: (1) a robust mechanism for extracting pertinent, machine-readable clinical data from Electronic Health Records (EHRs) across multiple institutions; and (2) a robust Artificial Intelligence (AI) platform that is capable of teasing apart the complex interplay between maternal factors, phenotypes, genotypes, gene functions and clinical outcomes. Here, we propose innovative solutions to these challenges, by assembling teams of content experts to leverage existing infrastructure to extract relevant outcomes directly from the EHR of participating PCGC Centers and by designing best-practice AI tools for outcomes research. Our principal goal is provide the vision, infrastructure and expertise to collaboratively empower CHD outcomes research, foster knowledge exchange, and train the next generation of genomic scientists. We propose to leverage existing data infrastructure to obtain Electronic Health Records (EHR) and other clinical variables at scale by partnering with other research networks to create a PCGC Data Resource. Using this resource, we will create and deploy a platform of Artificial Intelligence (AI)-based predictors for CHD outcomes research, with the goal of translating genomic discoveries into improved management and therapeutic strategies for CHD.