Diagnosing the Unknown for Care and Advancing Science (DUCAS)
诊断未知的护理和推进科学 (DUCAS)
基本信息
- 批准号:10682163
- 负责人:
- 金额:$ 470.51万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-04-19 至 2028-03-31
- 项目状态:未结题
- 来源:
- 关键词:AddressAmalgamAutomationBioinformaticsBiomedical ResearchCaringClinicClinicalClinical ResearchConsentDataData AnalysesData Coordinating CenterDiagnosisDiagnosticDiagnostics ResearchDiseaseEcosystemEnsureEnvironmentEtiologyEvaluationFamilyGenomicsGoalsHealthcareHealthcare SystemsIndustry StandardInfrastructureInstitutionKnowledgeLeadMachine LearningMedicalNomenclaturePatient ParticipationPatient advocacyPatient-Focused OutcomesPatientsPersonsProcessProductivityResearchResearch PersonnelResearch SupportSchemeScienceServicesTherapeuticTranslational ResearchUnited States National Institutes of Healthadvocacy organizationsclinical caredata managementdata science managementdata sharingdesignexperiencefollow-upgenome-widemedical schoolsmultidisciplinarynovel diagnosticsoutreachpatient populationpatient screeningprogramsrare conditionremediationresponsescale uptimeline
项目摘要
PROJECT SUMMARY
In our current healthcare system, it often takes years before patients with rare conditions and rare
presentations of common conditions receive a diagnosis. In 2013, the National Institutes of Health supported
the creation of the Undiagnosed Diseases Network (UDN) to address the needs of these patients, to facilitate
the diagnostic process for those with undiagnosed conditions and generate new knowledge about underlying
mechanisms of disease. The UDN was successful in solving medical mysteries, shortening diagnostic
odysseys, and contributing to biomedical research discovery. In order to serve more patients, the UDN process
must be scaled and integrated into broader healthcare and research ecosystems. As the Data Management
Coordinating Center (DMCC) for a network of Diagnostic Centers of Excellence, Harvard Medical School will
leverage experience in the UDN to create sustainable, nationally scaled infrastructure to support diagnosis,
research, and care for those who are undiagnosed. This will be accomplished by bringing together experts in
trans-institutional data sharing, data analysis, clinical care, bioinformatics, novel diagnostics, and translational
research and creating three DMCC Cores - Administrative, Data Management, and Clinical Research Support -
to address unmet needs of the undiagnosed. The Administrative Core will unite the DMCC and support
activities of all three Cores. Together, the DMCC Cores will accomplish four aims: 1) Scale up UDN throughput
by at least an order of magnitude to meet a pressing national need, 2) Leverage partnerships for sustainable
coordination of diagnostic processes to increase patient autonomy while advancing opportunities for
investigative science, 3) Maximize data mobility, interpretability, and shareability, and 4) Provide analytic
service and data stewardship through the Data Management Core and Clinical Research Support Cores led by
experts in genomics and AI teaming with clinicians and researchers.
项目摘要
在我们目前的医疗保健系统中,罕见疾病和罕见疾病的患者通常需要数年时间才能获得治疗。
常见疾病的介绍接受诊断。2013年,美国国立卫生研究院支持
建立未诊断疾病网络(UDN),以满足这些患者的需求,
为那些未确诊的疾病的诊断过程,并产生新的知识,
疾病的机制。UDN成功地解决了医学谜团,缩短了诊断时间,
奥德赛,并有助于生物医学研究发现。为了服务更多的患者,UDN流程
必须扩大规模并整合到更广泛的医疗保健和研究生态系统中。作为数据管理
协调中心(DMCC)的卓越诊断中心网络,哈佛医学院将
利用UDN的经验,创建可持续的、全国规模的基础设施,以支持诊断,
研究和照顾那些未确诊的人。这将通过召集专家来实现,
跨机构数据共享、数据分析、临床护理、生物信息学、新型诊断和翻译
研究和创建三个DMCC核心-行政,数据管理和临床研究支持-
来满足未确诊患者的需求行政核心将联合DMCC并支持
三个核心的活动。DMCC核心将共同实现四个目标:1)扩大UDN吞吐量
至少提高一个数量级,以满足紧迫的国家需求,2)利用伙伴关系,
协调诊断过程,以提高患者的自主性,同时增加
调查科学,3)最大限度地提高数据的移动性,可解释性和可共享性,以及4)提供分析
通过数据管理核心和临床研究支持核心提供服务和数据管理,
基因组学和人工智能专家与临床医生和研究人员合作。
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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Euan A Ashley其他文献
Artificial Intelligence in Molecular Medicine. Reply.
分子医学中的人工智能。
- DOI:
10.1056/nejmc2308776 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Bruna Gomes;Euan A Ashley - 通讯作者:
Euan A Ashley
Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modelling study of hospital data.
通过人工智能增强心脏 MRI 预测诊断和舒张充盈压:医院数据的建模研究。
- DOI:
10.1016/s2589-7500(24)00063-3 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
D. Lehmann;Bruna Gomes;Niklas Vetter;Olivia Braun;Ali Amr;Thomas Hilbel;Jens Müller;Ulrich Köthe;Christoph Reich;E. Kayvanpour;F. Sedaghat;Manuela Meder;J. Haas;Euan A Ashley;Wolfgang Rottbauer;D. Felbel;Raffi Bekeredjian;H. Mahrholdt;Andreas Keller;P. Ong;Andreas Seitz;H. Hund;N. Geis;F. André;Sandy Engelhardt;Hugo A Katus;Norbert Frey;Vincent Heuveline;Benjamin Meder - 通讯作者:
Benjamin Meder
Euan A Ashley的其他文献
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{{ truncateString('Euan A Ashley', 18)}}的其他基金
Diagnosing the Unknown for Care and Advancing Science (DUCAS)
诊断未知的护理和推进科学 (DUCAS)
- 批准号:
10872436 - 财政年份:2023
- 资助金额:
$ 470.51万 - 项目类别:
Systematically mapping variant effects for cardiovascular genes
系统地绘制心血管基因的变异效应
- 批准号:
10501975 - 财政年份:2022
- 资助金额:
$ 470.51万 - 项目类别:
Center for Undiagnosed Diseases at Stanford Administrative Supplement
斯坦福大学未确诊疾病中心行政增刊
- 批准号:
10677455 - 财政年份:2022
- 资助金额:
$ 470.51万 - 项目类别:
Structure function relationships from deep mutational scanning in human cardiomyopathy
人类心肌病深度突变扫描的结构功能关系
- 批准号:
10083762 - 财政年份:2020
- 资助金额:
$ 470.51万 - 项目类别:
Structure function relationships from deep mutational scanning in human cardiomyopathy
人类心肌病深度突变扫描的结构功能关系
- 批准号:
10576926 - 财政年份:2020
- 资助金额:
$ 470.51万 - 项目类别:
Structure function relationships from deep mutational scanning in human cardiomyopathy
人类心肌病深度突变扫描的结构功能关系
- 批准号:
9884435 - 财政年份:2020
- 资助金额:
$ 470.51万 - 项目类别:
Structure function relationships from deep mutational scanning in human cardiomyopathy
人类心肌病深度突变扫描的结构功能关系
- 批准号:
10364603 - 财政年份:2020
- 资助金额:
$ 470.51万 - 项目类别:
What comes next? Engaging stakeholders in governance of participant data and relationships during the sunset of large genomic medicine research initiatives
接下来是什么?
- 批准号:
10162151 - 财政年份:2018
- 资助金额:
$ 470.51万 - 项目类别:
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