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：重症监护协作资源翻译科学、信息学、 综合分析和学习 人工智能(AI)的翻译研究因缺乏与 有足够的深度、广度和多样性。普通民众可免费获得的电子病历数据集非常有限 研究社区，特别是人工智能研究社区，通过基于凭据的访问。模拟数据集为 来自单一机构，具有固定和有限的种族、民族和地理特征。EICU数据集为 数据全面性有限(例如，实验室测试的种类~模拟的五分之一)，数据跨度(1年，2014- 2015)和数据多样性(例如，没有自由文本的临床记录)等。因此，MIMIME和EICU分别具有优势 以及数据深度和数据广度方面的不足。 这项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 以开发和评估多站点非识别ICU数据集，以促进加速翻译研究 在人工智能和深度学习方法中，了解、跟踪和预测患者的病理生理状态。在……里面 这个项目，一组全国性的CTSA站点将共同构建一个新的、更具包容性的、多站点的数据集 研究人员可以从NCATS云下载基于凭据的访问权限。这个项目将结合 MIMIC(数据深度)和EICU(数据广度)各自的优势。创建的数据集将包括更多内容 地理区域、大量时间序列数据，包括ICU前、ICU中和ICU后 信息。这不仅将纳入更多的患者多样性，而且还将体现地区人口差异。 并实践可能会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据访问，该数据集包括未识别的离散数据集 各CTSA中危重病人的门诊、住院和ICU数据。AIM 2将创建联合访问数据集 从多站点ICU数据中提取并开发新的联合学习方法，包括 用于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， 罕见疾病患者)。Aim 3将开发基于记忆网络的新型元学习人工智能算法，并使用 多点数据集，以回答重症监护中具体和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，并开发 其他CTSA加入这一努力的最佳做法指南。特别是，我们的目标是支持和培育 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建立针对重症监护的大型跨CTSA协作数据共享 CTSA协作网络。由于上述CTSA的种族、民族和地理概况各不相同， 我们将能够支持公平和可推广的算法，用于先进的患者监控和决策支持。 拟议的项目将为全国提供最佳实践指导并树立示范榜样 CTSA。它还将支持培养下一代医学人工智能研究人员。