Data Integration and Quality Core
数据集成和质量核心
基本信息
- 批准号:10274378
- 负责人:
- 金额:$ 16.63万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-09-30 至 2026-05-31
- 项目状态:未结题
- 来源:
- 关键词:Artificial IntelligenceClinical DataClinical ResearchCollaborationsCommon Data ElementCountryDataData AnalysesData CollectionData Management ResourcesDevelopmentDevicesElderlyElectronic Health RecordEnsureEvaluationFast Healthcare Interoperability ResourcesFeedsGoalsHealthInterventionLaboratoriesMachine LearningModalityNCI ThesaurusOutcomePatient riskPatientsPersonal SatisfactionPhysicsPilot ProjectsPublic Health SchoolsReportingResearchResearch PersonnelResourcesSecureSemanticsServicesSourceSurveysSystemSystems AnalysisTechnologyTerminologyUnified Medical Language Systembasecloud baseddata frameworkdata integrationdata qualityexperienceflexibilityheuristicshigh standardimprovedinquiry-based learningmHealthmedical schoolsprecision medicineresiliencesensortechnology development
项目摘要
The goals of the Johns Hopkins AITC are profoundly data intensive, and in most cases will involve large
efforts that incorporate disparate clinical data. The Data Integration and Quality Core will facilitate the
connections between pilot study investigators and appropriate data connection needed to develop new
and marketable products that improve the health of older adults. The spectrum of patient risks,
intervention parameters, and outcomes comprise a large swath of electronic health record (EHR) data.
The aims of this proposal are 1) to ensure that all supported AITC projects are reviewed and optimized to
the highest standards of data quality and utilization building on the data quality and management
resources available across the Johns Hopkins School of Medicine and the School of Public Health, 2) to
provide a common platform for disparate data consolidation and integration, leveraging available
resources at Hopkins ideally suited for this purpose. The Johns Hopkins Precision Medicine Platform
(PMAP) provides a secure, robust, and flexible cloud-based framework for data integration and analyses.
Our core will review all concept proposals and pilot applications and help to ensure, and 3) to harmonize
common data elements across sources and domains into a canonical standard where practical. We will
use the OHDSI-OMOP standards enriched with HL7 FHIR feeds for Electronic Health Record data, Open
mHealth and CommonHealth for device data integration, and Common Terminology Services enhance
FHIR Terminology Server functionality augmented with UMLS, caDSR, and the NCI Thesaurus for
semantic data integration. Completion of these aims will help to ensure that related modalities of data
including patient reported information, surveys, and sensor data will be integrated into coherent renderings
that can sustain inferencing for machine learning discovery or statistical evaluation. We will also help to
assure that any AI or technology related data collected as part of any artificial intelligence or technology
development application that comes thru this AITC will be vetted and organized in such a way that it can
be quickly utilized in the development of specific products that are meant to improve the health and well-
being of older adults. Important in this effort is the development of the Johns Hopkins Precision Medicine
Analytics Platform (PMAP), a data collection and analysis system built for approved clinical research
based upon clinical data of patients was developed and is maintained as a collaboration between the
Johns Hopkins School of Medicine and the Johns Hopkins Applied Physics Laboratory to accelerate
biomedical discovery. Our experience in the development and implementation this data platform will
enable pilot study investigators from across the country. Building on this, and expertise in data platform
and electronic health record research, we propose to support the development and completion of all pilot
projects within the JH AITC according to the following specific aims.
约翰霍普金斯大学AITC的目标是深度数据密集型的,并且在大多数情况下将涉及大型
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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{{ truncateString('CHRISTOPHER G CHUTE', 18)}}的其他基金
Iron-CLAD: securely advancing AoU participant characterization with provenplatforms and collaborations
Iron-CLAD:通过经过验证的平台和协作安全地推进 AoU 参与者特征描述
- 批准号:
10829135 - 财政年份:2023
- 资助金额:
$ 16.63万 - 项目类别:
Johns Hopkins Training Program in Biomedical Informatics and Data Science
约翰霍普金斯大学生物医学信息学和数据科学培训计划
- 批准号:
10406045 - 财政年份:2022
- 资助金额:
$ 16.63万 - 项目类别:
Johns Hopkins Training Program in Biomedical Informatics and Data Science
约翰霍普金斯大学生物医学信息学和数据科学培训计划
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10620202 - 财政年份:2022
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Computational LOINC to Support Biomedical Research at Scale
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10395413 - 财政年份:2021
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Computational LOINC to Support Biomedical Research at Scale
计算 LOINC 支持大规模生物医学研究
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10610911 - 财政年份:2021
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A National Center for Digital Health Informatics Innovation
国家数字健康信息学创新中心
- 批准号:
10437464 - 财政年份:2021
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CD2H - 国家新冠肺炎队列协作 (N3C)
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A National Center for Digital Health Informatics Innovation
国家数字健康信息学创新中心
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10464821 - 财政年份:2021
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$ 16.63万 - 项目类别:
Computational LOINC to Support Biomedical Research at Scale
计算 LOINC 支持大规模生物医学研究
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10093337 - 财政年份:2021
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