PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP)

项目总结： 父母在心脏骤停-ICECAP(PRECICECAP)研究中的精确护理的目标是 心脏骤停后发现新的生物标志物信号可预测治疗反应和 长期复苏。心脏骤停是一个主要的公共卫生问题，发病率高， 死亡率。提高存活率和功能恢复是关键的公共卫生目标。我们 假设并不是所有的患者都是相同的，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，以识别不同的患者亚群。 PRECICECAP分析计划允许我们满足我们的研究需求，但不是本质上的 可概括的或可被其他人使用的。越来越多的NIH资助的项目积累并 分析住院患者的相关数据集，并开发他们自己的定制解决方案。是这样的 一种特别的方法成本高、效率低，并威胁到研究的严密性和重复性。这个 PRECICECAP本附录的目标是开发一个免费可用的软件平台，该平台 允许对复杂的神经危重病护理数据进行人工智能/机器学习(AI/ML)分析 并提供来自PRECICECAP的精选数据集，供AI/ML使用。在这里学到的知识 将被应用于帮助实现NIH生物医学研究数据生态系统现代化的目标 通过开发一种软件产品，可以在这种复杂的神经危重护理中处理AI/ML 数据。它还将允许共享一个干净的、带注释的综合数据集。通过一个 临床医生研究人员、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，可供进行类似操作的其他人使用 研究，推进NIH的使命，使复杂的数据公平(可查找，可访问， 可互操作和可重复使用)。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)，允许用户以图形方式构建 处理管道，在适当的情况下最大限度地实现自动化，并允许与 在需要时提供数据。我们将开发一个用户友好的仪表板界面，以允许个人学习 站点和协调中心，以了解复杂数据、检查关键元数据功能，以及 找出潜在的错误。模块化设计便于AI/ML体系结构的动态配置 在同一界面内，允许各个模块协同组合，以最大限度地 效率和重复性。这一结果将促进科学家之间开放、广泛的合作 使用相似的数据。

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.