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: 10461229
• 负责人: JAMES J CIMINO
• 金额: $ 120.3万
• 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: 2021-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10461229
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Admission activity; Algorithms; Artificial Intelligence; Benchmarking; Categories; Clinical; Clinical Trials; Communities; Community Health Education; Credentialing; Critical Care; Critical Illness; Data; Data Set; Data Sources; Disadvantaged; Disease; Elements; Environment; Fast Healthcare Interoperability Resources; Foundations; Geographic Locations; Geography; Graph; Growth; Health; Health Insurance Portability and Accountability Act; Informatics; Inpatients; Institution; Intensive Care; Intervention; Lead; Learning; Libraries; Machine Learning; Medical; Memory; Methodology; Methods; Modeling; National Center for Advancing Translational Sciences; Network-based; Nosocomial pneumonia; Outcome; Outpatients; Patient Care; Patient Monitoring; Patient-Focused Outcomes; Patients; Phenotype; Population; Practice Guidelines; Rare Diseases; Research; Research Personnel; Resources; Risk; Series; Site; Structure; Testing; Text; Time; Training and Education; Translational Research; Translations; Variant; Vision; Width; Work; artificial intelligence algorithm; autoencoder; base; clinical application; clinical translation; course module; data modeling; data sharing; data sharing networks; deep learning; deep learning model; driving force; federated learning; firewall; high risk; innovation; learning strategy; machine learning model; multimodality; next generation; novel; open source; programs; racial and ethnic; racial diversity; repository; structured data

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：重症监护协作资源转化科学，信息学， 全面的分析和学习 人工智能（AI）的转化研究一直受到缺乏共享数据资源的阻碍， 足够的深度、广度和多样性。有非常有限的EHR数据集免费提供给一般 研究社区，特别是人工智能研究社区通过基于凭证的访问。MIMIC数据集是 从一个单一的机构，有一个固定的和有限的种族，民族和地理概况。eICU数据集是 数据全面性有限（例如，实验室测试的种类数量约为MIMIC的1/5），数据跨度（1年，2014- 2015），以及数据多样性（例如，因此MIMIC和eICU分别具有优势 以及数据深度和数据广度的缺点。 该提案的愿景是利用具有不同种族、族裔和地理特征的多个CTSA， 为了开发和评估多中心去识别ICU数据集，以促进加速转化研究 在人工智能和深度学习方法来理解，跟踪和预测患者的病理生理状态。在 在这个项目中，一组全国性的CTSA站点将共同努力，建立一个新的、更具包容性的多站点数据集 它可以由研究人员通过基于证书的访问从NCATS云下载。这个项目将联合收割机 MIMIC（数据深度）和eICU（数据广度）的各自优势。创建的数据集将包含更多 地理区域，大量时间序列数据，包括ICU患者术前、术中和术后 信息.这不仅将纳入更多的患者多样性，还将捕捉区域人群差异 以及可能产生临床影响的实践变化。 Aim 1将开发并提供对多站点数据集的凭证访问，该数据集由去识别的离散数据组成。 各CTSA的重症患者的门诊、住院和ICU数据。Aim 2将创建联邦访问数据集 从多地点ICU数据中提取并开发新型联邦学习方法，其中包括 用于具有较高重新识别风险的选定患者组的非结构化临床记录或结构化数据（例如， 罕见病患者）。目标3将开发基于记忆网络的新型元学习AI算法，并使用 多站点数据集，以回答重症监护中的具体和长期存在的临床问题。Aim 4将创新性地 利用图书馆网络为研究界开发和传播开放资源， 最佳实践指南，供其他CTSA加入这一努力。特别是，我们的目标是支持和培育增长 下一代医疗人工智能劳动力的研究和实践。 我们的目标是通过利用现有的 CTSA协作网络。由于上述CTSA的种族、族裔和地理分布多样化， 我们将能够支持用于高级患者监测和决策支持的公平和可推广的算法。 拟议的项目将为全国范围内提供最佳做法指导，并树立典范。 CTSA。它还将支持培养下一代医疗AI研究人员。