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：重症监护合作资源转化科学、信息学、 综合分析和学习 人工智能（AI）的转化研究因缺乏与人工智能（AI）共享的数据资源而受到阻碍。 足够的深度、广度和多样性。可供公众免费使用的 EHR 数据集非常有限 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 来自具有固定且有限的种族、民族和地理特征的单一机构。 eICU 数据集是 数据全面性有限（例如，实验室测试种类数量~MIMIC的1/5）、数据跨度（1年，2014年- 2015），以及数据多样性（例如，没有自由文本临床笔记）等。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、民族和地理特征的多个 CTSA 为了开发和评估多站点去识别化 ICU 数据集，以促进加速转化研究 利用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 在该项目中，一组全国性的 CTSA 站点将共同构建一个新的、更具包容性的多站点数据集 研究人员可以通过基于凭证的访问权限从 NCATS 云下载该文件。该项目将结合 MIMIC（数据深度）和eICU（数据广度）各自的优势。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 患者入住前、入住期间和入住后 信息。这不仅会体现更多的患者多样性，还会体现区域人口差异 并练习可能产生临床影响的变化。 目标 1 将开发并提供对多站点数据集的凭证访问，该数据集由去识别化的离散数据组成 各 CTSA 的重症患者的门诊、住院和 ICU 数据。目标 2 将创建联合访问数据集 从多站点 ICU 数据中开发新颖的联合学习方法，其中包括 针对重新识别风险较高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）。 Aim 3 将开发新颖的基于记忆网络的元学习人工智能算法，并使用 多站点数据集，用于回答重症监护中具体且长期存在的临床问题。目标4将创新 利用图书馆网络为研究界开发和传播开放资源，并发展 为其他 CTSA 加入这一努力提供最佳实践指南。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。 我们的目标是利用现有的基础设施，建立一个大型的跨 CTSA 重症监护协作数据共享平台。 CTSA 协作网络。由于上述 CTSA 具有多样化的种族、民族和地理特征， 我们将能够支持公平且可推广的算法，以实现高级患者监测和决策支持。 拟议项目将为全国范围内的最佳实践提供指导并树立典范 CTSA。它还将支持下一代医疗人工智能研究人员的培养。