CRITICAL: Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning
关键:重症监护转化科学、信息学、综合分析和学习的协作资源
基本信息
- 批准号:10461229
- 负责人:
- 金额:$ 120.3万
- 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:2021-08-15 至 2025-07-31
- 项目状态:未结题
- 来源:
- 关键词:Acute Renal Failure with Renal Papillary NecrosisAdmission activityAlgorithmsArtificial IntelligenceBenchmarkingCategoriesClinicalClinical TrialsCommunitiesCommunity Health EducationCredentialingCritical CareCritical IllnessDataData SetData SourcesDisadvantagedDiseaseElementsEnvironmentFast Healthcare Interoperability ResourcesFoundationsGeographic LocationsGeographyGraphGrowthHealthHealth Insurance Portability and Accountability ActInformaticsInpatientsInstitutionIntensive CareInterventionLeadLearningLibrariesMachine LearningMedicalMemoryMethodologyMethodsModelingNational Center for Advancing Translational SciencesNetwork-basedNosocomial pneumoniaOutcomeOutpatientsPatient CarePatient MonitoringPatient-Focused OutcomesPatientsPhenotypePopulationPractice GuidelinesRare DiseasesResearchResearch PersonnelResourcesRiskSeriesSiteStructureTestingTextTimeTraining and EducationTranslational ResearchTranslationsVariantVisionWidthWorkartificial intelligence algorithmautoencoderbaseclinical applicationclinical translationcourse moduledata modelingdata sharingdata sharing networksdeep learningdeep learning modeldriving forcefederated learningfirewallhigh riskinnovationlearning strategymachine learning modelmultimodalitynext generationnovelopen sourceprogramsracial and ethnicracial diversityrepositorystructured data
项目摘要
CRITICAL: Collaborative Resource for Intensive care Translational science, Informatics,
Comprehensive Analytics, and Learning
Translational research in Artificial Intelligence (AI) has been hindered by the lack of shared data resources with
sufficient depth, breadth and diversity. There are very limited EHR datasets freely available to the general
research community especially the AI research community through credential-based access. MIMIC dataset is
from a single institution that has a fixed and limited racial, ethnic and geographic profile. The eICU dataset is
limited in data comprehensiveness (e.g., number of kinds of lab tests ~1/5 of MIMIC), data span (1 year, 2014-
2015), and data variety (e.g., no free text clinical notes) etc. Thus MIMIC and eICU respectively have advantages
and disadvantages of data depth and data breadth.
The vision of this proposal is to leverage multiple CTSAs with diverse racial, ethnic and geographic profiles in
order to develop and evaluate a multi-site de-identified ICU dataset, to facilitate accelerate translational research
in AI and deep learning approaches to understand, track, and predict the pathophysiological state of patients. In
this project, a group of nationwide CTSA sites will work together to build a new, more inclusive, multi-site dataset
that is downloadable from NCATS cloud by researchers with credential-based access. This project will combine
the respective advantages of MIMIC (data depth) and eICU (data breadth). The created dataset will include more
geographic regions, larger quantities of time-series data, including pre-, during- and post- ICU patient
information. This will incorporate not only more patient diversity, but also capture regional population differences
and practice variations that could have clinical impact.
Aim 1 will develop and provide credentialed access to a multi-site dataset consisting of de-identified discrete
outpatient, inpatient, and ICU data for critically ill at respective CTSAs. Aim 2 will create federated access dataset
from and develop novel federated learning methods on the part of the multi-site ICU data consisting of
unstructured clinical notes or structured data for select group of patients at higher risks of re-identification (e.g.,
rare disease patients). Aim 3 will develop novel memory-network based meta-learning AI algorithms and use the
multi-site dataset to answer concrete and long-standing clinical problems in critical care. Aim 4 will innovatively
leverage the library network to develop and disseminate open resources for the research community and develop
best practice guidelines for other CTSAs to join the effort. In particular, we aim to support and cultivate the growth
of next generation medical AI workforce for research and practice.
We aim to establish a large cross-CTSA collaborative data sharing for critical care by leveraging the existing
CTSA collaborative networks. With the diversified racial, ethnic and geographic profiles from the above CTSAs,
we will be able to support fair and generalizable algorithms for advanced patient monitoring and decision support.
The proposed project will provide best practice guidance to and set up exemplary examples for nationwide
CTSAs. It will also support the cultivation of next generation medical AI researchers.
关键字:重症监护合作资源转化科学,信息学,
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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JAMES J CIMINO其他文献
JAMES J CIMINO的其他文献
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{{ truncateString('JAMES J CIMINO', 18)}}的其他基金
Integrating Genomic Risk Assessment for Chronic Disease Management in a Diverse Population
整合基因组风险评估以进行不同人群的慢性病管理
- 批准号:
10852376 - 财政年份:2023
- 资助金额:
$ 120.3万 - 项目类别:
Improving Electronic Health Record Usability and Usefulness with a Patient-Specific Clinical Knowledge Base
通过患者特定的临床知识库提高电子健康记录的可用性和实用性
- 批准号:
10155135 - 财政年份:2021
- 资助金额:
$ 120.3万 - 项目类别:
CRITICAL: Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning
关键:重症监护转化科学、信息学、综合分析和学习的协作资源
- 批准号:
10673051 - 财政年份:2021
- 资助金额:
$ 120.3万 - 项目类别:
Improving Electronic Health Record Usability and Usefulness with a Patient-Specific Clinical Knowledge Base
通过患者特定的临床知识库提高电子健康记录的可用性和实用性
- 批准号:
10458471 - 财政年份:2021
- 资助金额:
$ 120.3万 - 项目类别:
CRITICAL: Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning
关键:重症监护转化科学、信息学、综合分析和学习的协作资源
- 批准号:
10300398 - 财政年份:2021
- 资助金额:
$ 120.3万 - 项目类别:
Integrating Genomic Risk Assessment for Chronic Disease Management in a Diverse Population
整合基因组风险评估以进行不同人群的慢性病管理
- 批准号:
10207721 - 财政年份:2020
- 资助金额:
$ 120.3万 - 项目类别:
Integrating Genomic Risk Assessment for Chronic Disease Management in a Diverse Population
整合基因组风险评估以进行不同人群的慢性病管理
- 批准号:
10650794 - 财政年份:2020
- 资助金额:
$ 120.3万 - 项目类别:
Integrating Genomic Risk Assessment for Chronic Disease Management in a Diverse Population
整合基因组风险评估以进行不同人群的慢性病管理
- 批准号:
10447819 - 财政年份:2020
- 资助金额:
$ 120.3万 - 项目类别:
Integrating Genomic Risk Assessment for Chronic Disease Management in a Diverse Population
整合基因组风险评估以进行不同人群的慢性病管理
- 批准号:
10619261 - 财政年份:2020
- 资助金额:
$ 120.3万 - 项目类别:
Semantic and Machine Learning Methods for Mining Connections in the UMLS
UMLS 中挖掘连接的语义和机器学习方法
- 批准号:
7299922 - 财政年份:2007
- 资助金额:
$ 120.3万 - 项目类别: