PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP)

心脏骤停的精准护理 - ICECAP (PRECICECAP)

• 批准号: 10412861
• 负责人: Jonathan Elmer
• 金额: $ 32.9万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-15 至 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10412861
• 关键词: Acute Brain Injuries; Address; Architecture; Artificial Intelligence; Attention; Automation; Biomedical Research; Brain; Brain imaging; Cardiopulmonary; Cardiopulmonary Resuscitation; Caring; Cessation of life; Clinical; Collaborations; Common Data Element; Communities; Complex; Computer software; Critical Illness; Custom; Data; Data Aggregation; Data Scientist; Data Set; EEG-based imaging; FAIR principles; Funding; Goals; Heart; Heart Arrest; Human; Individual; Industry; Intensive Care; Knowledge; Label; Libraries; Lung; Machine Learning; Manuals; Metadata; Mission; Modern Medicine; Modernization; Monitor; Morbidity - disease rate; Neurologic; Neurosciences Research; Ontology; Organ; Outcome; Parents; Patients; Physiological; Public Health; Readiness; Recovery; Recovery of Function; Reproducibility; Research; Research Personnel; Resolution; Scientist; Series; Signal Transduction; Site; Software Tools; Source; Standardization; System; Techniques; Terminology; Testing; Time; United States National Institutes of Health; Visualization; Work; application programming interface; base; biomarker signature; complex data; cost; dashboard; data archive; data ecosystem; data harmonization; data pipeline; data visualization; design; disability; feature extraction; flexibility; graphical user interface; improved; innovation; mortality; natural hypothermia; novel; novel marker; patient subsets; personalized care; precision medicine; prospective; quality assurance; software development; supervised learning; tool; unsupervised learning; user-friendly

Project Summary: The goals of the parent PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP) study are to discover novel biomarker signatures after cardiac arrest that predict treatment responsiveness and long-term recovery. Cardiac arrest is a major public health problem with high morbidity and mortality. Improving survival and functional recovery are critical public health objectives. We hypothesize that not all patients are identical and that through innovative, multi-parametric data- driven approaches we will be able to identify novel signatures to identify distinct patient subgroups. The PRECICECAP analysis plan allows us to meet our study needs but is not intrinsically generalizable or usable by others. An ever-growing number of NIH-funded projects amass and analyze related datasets from hospitalized patients and develop their own custom solutions. Such an ad hoc approach is costly, inefficient, and threatens research rigor and reproducibility. The objective of this supplement to PRECICECAP is to develop a freely-available software platform that allows artificial intelligence/machine learning (AI/ML) analysis of complex neurocritical care data and to provide a curated dataset from PRECICECAP ready for AI/ML. The knowledge learned here will be applied to help achieve NIH goals for modernizing the biomedical research data ecosystem by developing a software product that can handle AI/ML on this type of complex neurocritical care data. It will also allow the sharing of a cleaned, annotated comprehensive data set. Through a thoughtful collaboration between clinician investigators, data scientists and industry, we will take NIH-supported data from the PRECICECAP study and make it broadly available and easily usable. The project will deliver an important software tool that can be used by others conducting similar research, advancing the NIH’s mission to make complex data FAIR (Findable, Accessible, Interoperable, and Reusable). We will develop a series of modular functions (for example, to support data harmonization, annotation or visualization) that permit users to graphically construct processing pipelines maximizing automation where appropriate and allowing facile interaction with data when needed. We will develop a user-friendly dashboard interface to allow individual study sites and coordinating hubs to understand complex data, inspect key meta-data features, and identify potential errors. Modular design facilitates dynamic configuration of AI/ML architectures within the same interface, allowing individual modules to combine synergistically to maximize efficiency and reproducibility. The result will facilitate an open, wide collaboration between scientists using similar data.

项目摘要： 母公司PRECision Care In Cardiac ArrEst - ICECAP（PRECICECAP）研究的目标是 发现心脏骤停后新的生物标志物特征，预测治疗反应性， 长期复苏。心脏骤停是一个主要的公共卫生问题，发病率高， mortality.改善生存和功能恢复是关键的公共卫生目标。我们 假设并非所有患者都是相同的，通过创新的多参数数据- 驱动的方法，我们将能够识别新的签名，以确定不同的患者亚组。 PRECICECAP分析计划使我们能够满足我们的研究需求，但本质上不是 可被其他人推广或使用的。越来越多的NIH资助的项目积累和 分析住院患者的相关数据集，并开发自己的定制解决方案。等 特别的方法成本高、效率低，并威胁到研究的严谨性和可重复性。的 PRECICECAP这一补充的目的是开发一个免费提供的软件平台， 允许人工智能/机器学习（AI/ML）分析复杂的神经重症监护数据 并提供PRECICECAP为AI/ML准备的精选数据集。在这里学到的知识 将用于帮助实现NIH的目标，使生物医学研究数据生态系统现代化 通过开发一种软件产品，可以在这种复杂的神经重症监护中处理AI/ML， 数据它还将允许共享一个经过清理、附加说明的综合数据集。通过 临床研究人员，数据科学家和行业之间的深思熟虑的合作，我们将采取 NIH支持的PRECICECAP研究数据，并使其广泛可用和易于使用。 该项目将提供一个重要的软件工具，可用于其他人进行类似的 研究，推进国家卫生研究院的使命，使复杂的数据公平（可发现，可解释， 可互操作和可重用）。我们将开发一系列模块化功能（例如， 支持数据协调、注释或可视化），其允许用户以图形方式构造 处理管道在适当的情况下最大限度地提高自动化程度，并允许与 需要时的数据。我们将开发一个用户友好的仪表板界面，以允许个人研究 站点和协调中心，以了解复杂数据，检查关键元数据功能， 识别潜在的错误。模块化设计促进AI/ML架构的动态配置 在同一界面内，允许各个模块协同联合收割机， 效率和再现性。这一结果将促进科学家之间的公开、广泛的合作 使用类似的数据。