Data Management Core

数据管理核心

• 批准号: 10488278
• 负责人: Sanjiv J Shah
• 金额: $ 34.9万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-13 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10488278
• 关键词: Artificial Intelligence; Bioinformatics; Biological; Biological Markers; Cardiac; Characteristics; Clinical; Communication; Communities; Coupled; Data; Data Coordinating Center; Data Pooling; Data Reporting; Data Sources; Detection; Development; Discipline; Disease; EFRAC; Echocardiography; Electrocardiogram; Electronic Health Record; Ensure; Failure; Foundations; Functional disorder; Goals; Health Technology; Heart failure; Home; Image; Individual; Investigation; Kidney; Lead; Leadership; Lipids; Machine Learning; Modeling; Molecular Probes; Monitor; Multicenter Studies; National Heart, Lung, and Blood Institute; Natural Language Processing; Outcome; Pathway interactions; Patient Self-Report; Patients; Phenotype; Procedures; Reporting; Research; Resistance; Resource Sharing; Resources; Risk Factors; Structure; Syndrome; Systems Biology; Techniques; Therapeutic; Trans-Omics for Precision Medicine; Translations; Ventricular; Visualization; Work; advanced analytics; autism spectrum disorder; automated image analysis; base; biobank; cardiometabolism; cardiovascular disorder risk; career; catalyst; cloud storage; cohort; data management; data portal; data resource; data standards; deep learning; disorder subtype; experience; experimental study; image archival system; innovation; mHealth; multiple omics; novel; open data; operation; phenotypic data; precision medicine; preservation; programs; prospective; statistics; therapeutically effective; tool

PROJECT SUMMARY The Northwestern HeartShare Data Translation Center (DTC) Data Management Core will leverage prior leadership and experience in the field of machine learning for the identification of novel heart failure with preserved ejection fraction (HFpEF) subtypes, along with a track record of leading data management for multi- center studies, to facilitate multi-dimensional, multi-omics analyses (including machine learning/artificial intelligence) for the entire HeartShare program. The HeartShare Data Management Core includes a highly integrated team of individuals from a variety of disciplines with expertise in data coordinating center operations, statistics, machine learning, omics, and HFpEF. These experts will work in concert to achieve the following specific aims: (1) to develop procedures and standards for data pooling, cleaning, and harmonization of all data from cohorts and trials; compile the data as a shared resource for the research community; and host these data on BioData Catalyst as part of the retrospective component of HeartShare; (2) to integrate self-reported data, home monitoring data from mobile health (mHealth) technologies, environmental data, electronic health record (EHR) data (including natural language processing [NLP] techniques), deep phenotyping data, and outcome data into a core data resource for the prospective component of HeartShare; (3) to create integrated biorepository and imaging repository resources; coordinate these resources with TOPMed; and facilitate automated deep learning analyses of images relevant to HeartShare; and (4) plan and facilitate advanced analytics (multi-omics and machine learning analyses) to identify HFpEF subtypes and the pathobiological basis underlying these subtypes. The Data Management Core will also ensure efficient visualization, reporting, and communication of findings; and we will work with BioData Catalyst experts in our Data Portal Core to transition publicly available data into cloud storage, implement governance within BioData Catalyst, and make tools and workflows cloud compatible.

项目摘要 西北的心脏数据翻译中心（DTC）数据管理核心将利用先验 在机器学习领域的领导和经验，以识别新的心力衰竭 保留的射血分数（HFPEF）子类型，以及用于多个数据管理的往绩记录 中心研究，以促进多维的多摩学分析（包括机器学习/人工 智力）整个心脏计划。心莎记数据管理核心包括高度 来自各个学科的个人综合团队，在数据协调中心运营方面具有专业知识， 统计，机器学习，OMIC和HFPEF。这些专家将共同努力实现以下内容 具体目的：（1）制定数据集合，清洁和协调所有数据的过程和标准 来自队列和审判；将数据作为研究社区共享资源；并托管这些数据 在生物催化剂上，作为心脏夏天回顾性组成部分的一部分； （2）整合自我报告的数据， 来自移动健康（MHealth）技术，环境数据，电子健康记录的家庭监控数据 （EHR）数据（包括自然语言处理[NLP]技术），深度表型数据和结果 将其数据作为心脏展望的前瞻性组成部分的核心数据资源； （3）创建集成的生物座席 和成像存储库资源；与Topmed协调这些资源；并促进自动化的深度 学习与心脏相关的图像的学习分析； （4）计划并促进高级分析（多摩学 和机器学习分析）以识别HFPEF亚型和病理生物学基础 亚型。数据管理核心还将确保有效的可视化，报告和通信 发现;我们将与数据门户核心的生物数据催化剂专家合作，以公开过渡 数据中的云存储，在生物数据催化剂中实现治理，并制作工具和工作流程云 兼容的。