Bridging the Gap between Genomics and Clinical Outcomes in CHD

缩小先心病基因组学与临床结果之间的差距

• 批准号: 10477466
• 负责人: MARTIN TRISTANI-FIROUZI
• 金额: $ 41.94万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10477466
• 关键词: Artificial Intelligence; Artificial Intelligence platform; Bayesian Network; Bioinformatics; Childhood; Clinical; Clinical Data; Clinical Research; Clinical and Translational Science Awards; Code; Collaborations; Complex; Custom; Data; Data Set; Dependence; Diagnostic; Disease Outcome; Electronic Health Record; Fostering; Foundations; Gender; Genes; Genomics; Genotype; Goals; Heart; Infrastructure; Institution; Interdisciplinary Study; Internet; Knowledge; Methodology; Modeling; Multicenter Studies; National Heart, Lung, and Blood Institute; Network-based; Online Systems; Outcome; Outcomes Research; Pathway interactions; Patients; Pediatric Cardiac Genomics Consortium; Pediatric cardiology; Phenotype; Readability; Recording of previous events; Research; Research Personnel; Resources; Risk Factors; Scientist; Site; Societies; Standard Model; Testing; Therapeutic; Thoracic Surgical Procedures; Training; Translating; Utah; Vision; Visit; Work; base; clinical care; clinical database; comorbidity; congenital heart disorder; data infrastructure; data resource; design; gene function; genomic data; improved; innovation; member; next generation; novel; outcome prediction; patient oriented; predict clinical outcome; programs; relational database; repository; skills; surgery outcome; tool

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.

项目总结/文摘