Bridge2AI: Patient-Focused Collaborative Hospital Repository Uniting Standards (CHoRUS) for Equitable AI
Bridge2AI:以患者为中心的协作医院存储库统一标准 (CHORUS),实现公平的人工智能
基本信息
- 批准号:10858694
- 负责人:
- 金额:$ 637.03万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-01 至 2026-08-31
- 项目状态:未结题
- 来源:
- 关键词:AccountabilityAcuteAddressAdoptionArtificial IntelligenceBiomedical ResearchBridge to Artificial IntelligenceCaringClinicalCollaborationsCommunitiesCritical CareCritical IllnessDataData ElementData SetData Storage and RetrievalDeteriorationDiagnosisDisciplineEducationElectroencephalographyElectronic Health RecordEngineeringEnsureEquityEthicsEventFocus GroupsFundingGenerationsGoalsHealth ServicesHospitalsImageIndustryInfrastructureJournalsLabelLawsLegalMachine LearningMeasuresMethodsModelingPatient-Focused OutcomesPatientsPrivacyPublicationsResearchResolutionSamplingScienceScientistStandardizationTelemetryTestingUnited States National Institutes of HealthValidationVisualizationWorkforce Developmentacute carecare deliverydata acquisitiondata modelingdata standardsdata toolselectronic structureimprovedliteracymultimodalityprogramsrepositoryskill acquisitionsocial health determinantstooltool developmenttreatment responsetrustworthiness
项目摘要
There is an urgent need for infrastructure to support artificial intelligence and machine learning (AI/ML) in critical care. Developing high-resolution multi-center data sets is a critical first step towards actionable and trustworthy AI. As part of the NIH Common Fund’s Bridge2AI program, the Patient-Focused Collaborative Hospital Repository Uniting Standards (CHoRUS) for Equitable AI data generation project will meet the need of generating data for ML/AI applications aimed at characterizing acute and critical care illness, predicting complications, and measuring treatment response among patients with acute or critical illness. Through 6 modules, the Patient-Focused CHoRUS for Equitable AI data generation project will addresses multiple challenges relevant for acquiring an AI-ready data set from more than 100,000 critically ill patients: 1) Team Science, 2) Ethical and Trustworthy AI, 3) Standards, 4) Tool Development and Optimization, 5) Data Acquisition, and 6) Skill and Workforce Development. The project’s overarching goal is to develop a publicly available, AI-ready critical care dataset of unprecedented diversity, while ensuring the methods promote privacy, accountability, clinical benefit, and equity, while promoting a new generation of AI clinicians and scientists. The dataset will also include a holdout test set, accessible for model external validation to aid marketplace adoption of AI-developed models for implementation in acute and critical care.
Drawing expertise from a diverse range of disciplines including team science, law, ethics, health services, biomedical science, engineering, and scientific journal publications, this project will A) establish a legal framework for collecting data at scale, sampling to ensure diversity and minimize bias; B) perform community-facing ethics focus groups to determine what data is appropriate for public sharing; C) ensure that data elements include appropriate social determinants of health to study and understand potential bias in care delivery; D) develop capabilities across a multi-center to acquire, standardize, tokenize, store, visualize, and label data including structured electronic health record data, tokenized unstructured electronic health record data, telemetry and EEG waveforms, imaging, and social determinants of health; E) acquire data, standardize data to the OMOP Common Data Model, transform data using differential privacy approaches that limit re-identification, and label data for diagnoses and events of clinical deterioration; and F) cultivate expertise in the lay and scientific community to improve AI literacy and utilization through multimodal educational approaches. To accomplish this, the project will involve extensive collaboration between centers as well as through the NIH Bridge2AI program, the NIH Bridge2AI Bridge Center, external biomedical and clinical organizations, industry, and regulatory agencies.
迫切需要基础设施来支持重症监护中的人工智能和机器学习(AI/ML)。开发高分辨率的多中心数据集是迈向可操作和可信赖的人工智能的关键第一步。作为NIH共同基金Bridge 2AI计划的一部分,以患者为中心的协作医院存储库联合标准(CHoRUS)公平AI数据生成项目将满足为ML/AI应用程序生成数据的需求,旨在描述急性和危重病,预测并发症,并测量急性或危重病患者的治疗反应。通过6个模块,以患者为中心的CHoRUS公平AI数据生成项目将解决与从10万多名重症患者中获取AI就绪数据集相关的多个挑战:1)团队科学,2)道德和值得信赖的AI,3)标准,4)工具开发和优化,5)数据采集,以及6)技能和劳动力发展。该项目的总体目标是开发一个公开可用的、具有前所未有多样性的AI就绪的重症监护数据集,同时确保这些方法促进隐私、问责制、临床效益和公平,同时促进新一代AI临床医生和科学家。该数据集还将包括一个保持测试集,可用于模型外部验证,以帮助市场采用人工智能开发的模型,用于在急性和重症监护中实施。
该项目将从团队科学、法律、伦理学、卫生服务、生物医学科学、工程学和科学期刊出版物等多个学科中汲取专业知识,A)建立一个法律的框架,用于大规模收集数据,抽样以确保多样性和最大限度地减少偏见; B)开展面向社区的伦理焦点小组,以确定哪些数据适合公众共享; C)确保数据要素包括适当的健康社会决定因素,以研究和理解护理提供中的潜在偏见; D)开发跨多中心的能力以获取、标准化、标记化、存储、可视化和标记数据,包括结构化电子健康记录数据、标记化的非结构化电子健康记录数据,E)获取数据,将数据标准化为OMOP公共数据模型,使用限制重新识别的差异隐私方法转换数据,并标记用于诊断和临床恶化事件的数据;和F)培养外行和科学界的专业知识,通过多模式教育方法提高人工智能素养和利用率。 为了实现这一目标,该项目将涉及中心之间的广泛合作,以及通过NIH Bridge 2AI项目,NIH Bridge 2AI Bridge中心,外部生物医学和临床组织,行业和监管机构。
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('Azra Bihorac', 18)}}的其他基金
Bridge2AI: Patient-Focused Collaborative Hospital Repository Uniting Standards (CHoRUS) for Equitable AI
Bridge2AI:以患者为中心的协作医院存储库统一标准 (CHORUS),实现公平的人工智能
- 批准号:
10472824 - 财政年份:2022
- 资助金额:
$ 637.03万 - 项目类别:
(MEnD-AKI) Multicenter Implementation of an Electronic Decision Support System for Drug-associated AKI
(MEnD-AKI) 药物相关 AKI 电子决策支持系统的多中心实施
- 批准号:
10414976 - 财政年份:2021
- 资助金额:
$ 637.03万 - 项目类别:
(MEnD-AKI) Multicenter Implementation of an Electronic Decision Support System for Drug-associated AKI
(MEnD-AKI) 药物相关 AKI 电子决策支持系统的多中心实施
- 批准号:
10594086 - 财政年份:2021
- 资助金额:
$ 637.03万 - 项目类别:
ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention
ADAPT:自主谵妄监测和适应性预防
- 批准号:
10396041 - 财政年份:2021
- 资助金额:
$ 637.03万 - 项目类别:
(MEnD-AKI) Multicenter Implementation of an Electronic Decision Support System for Drug-associated AKI
(MEnD-AKI) 药物相关 AKI 电子决策支持系统的多中心实施
- 批准号:
10609525 - 财政年份:2021
- 资助金额:
$ 637.03万 - 项目类别:
ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention
ADAPT:自主谵妄监测和适应性预防
- 批准号:
10178157 - 财政年份:2021
- 资助金额:
$ 637.03万 - 项目类别:
(MEnD-AKI) Multicenter Implementation of an Electronic Decision Support System for Drug-associated AKI
(MEnD-AKI) 药物相关 AKI 电子决策支持系统的多中心实施
- 批准号:
10209005 - 财政年份:2021
- 资助金额:
$ 637.03万 - 项目类别:
Intelligent Intensive Care Unit (I2CU): Pervasive Sensing and Artificial Intelligence for Augmented Clinical Decision-making
智能重症监护病房 (I2CU):普遍传感和人工智能增强临床决策
- 批准号:
10154047 - 财政年份:2021
- 资助金额:
$ 637.03万 - 项目类别:
Intelligent Intensive Care Unit (I2CU): Pervasive Sensing and Artificial Intelligence for Augmented Clinical Decision-making
智能重症监护病房 (I2CU):普遍传感和人工智能增强临床决策
- 批准号:
10580785 - 财政年份:2021
- 资助金额:
$ 637.03万 - 项目类别:
Intelligent Intensive Care Unit (I2CU): Pervasive Sensing and Artificial Intelligence for Augmented Clinical Decision-making
智能重症监护病房 (I2CU):普遍传感和人工智能增强临床决策
- 批准号:
10374834 - 财政年份:2021
- 资助金额:
$ 637.03万 - 项目类别:
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