Data Management Core

数据管理核心

• 批准号: 10678963
• 负责人: Sanjiv J Shah
• 金额: $ 30.34万
• 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/10678963
• 关键词: Artificial Intelligence; Bioinformatics; Biological; Biological Markers; Cardiac; Cardiovascular Diseases; Characteristics; Classification; Clinical; Communication; Communities; Coupled; Data; Data Coordinating Center; Data Pooling; Data Reporting; Data Sources; Dedications; Detection; Development; Dimensions; Discipline; Disease; Disparate; EFRAC; Echocardiography; Electrocardiogram; Electronic Health Record; Ensure; Foundations; Functional disorder; Goals; Health Technology; Heart failure; Home; Image; Individual; Investigation; Kidney; Leadership; Lipids; Machine Learning; Modeling; Molecular Probes; Monitor; Multicenter Studies; National Heart, Lung, and Blood Institute; Natural Language Processing; Organ; Outcome; Pathway interactions; Patient Self-Report; Patients; Phenotype; Procedures; Proliferating; Reporting; Research; Resistance; Resource Sharing; Resources; Structure; Syndrome; Systems Biology; Techniques; Therapeutic; Trans-Omics for Precision Medicine; Translations; Ventricular; Visualization; Work; advanced analytics; autism spectrum disorder; automated image analysis; biobank; cardiometabolism; cardiovascular risk factor; 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.

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.