Artificial Intelligence for Dynamic, individualized CPR guidance: AID CPR

项目摘要/摘要 院外心脏骤停是一个动态的过程，需要新的干预措施来改善 结果。呼气末二氧化碳(ETCO2)测量是一种被广泛认可、易于使用和 可以潜在地提供对正在进行的复苏努力的实时洞察；然而，它尚未应用于 个体化医疗。我们的总体假设是，将ETCO2二氧化碳二氧化碳描记术整合到uchA复苏中 将改善结果。使用创新的信号处理和机器学习方法，我们将识别出广泛的 复苏质量特征对复苏的影响范围及其与患者个体特征的关系 和可预见性。 这些目标将通过以下目标实现： 目的1.确定复苏干预对实时生理动力学和预后的影响 在uchA中。 目的2.建立个体患者特征对实时生理动力学和 HC A的结果。 目的3.开发一种基于实时个性化生理的心脏骤停复苏新策略 动力学。 我们将创建一个大型的心肺复苏过程数据存储库，其中包含来自 5300个成体uchA。这项工作将定义复苏期间ETCO2的内部动力学，以允许 开展有指导的复苏工作，由此产生的数据将为未来提供坚实的基础 假设驱动的研究。 Nassal博士的培训计划既包括正式的教学，也包括有经验的 将在信号处理和公平人工智能方面发展技能的导师和合作者 驱动算法。该团队在使用机器学习和多模式信号方面拥有丰富的经验 复苏过程中的分类和预测处理。这一培训计划将培养一套独特的技能 在高级心脏信号处理方面；人工智能，包括公平的机器学习处理； 以及在应用这些技能制定动态引导的复苏策略方面的专业知识 其他内科科学家拥有。

Project Summary/Abstract Out-of-hospital cardiac arrest (OHCA) is a dynamic process that requires new interventions to improve outcomes. End-tidal carbon dioxide (ETCO2) measurement is a tool that is widely recognized, easy to use, and can potentially provide real-time insights into ongoing resuscitation efforts; however, it has yet to be applied to individualized medicine. Our overall hypothesis is that integrating ETCO2 capnography into OHCA resuscitation will improve outcomes. Using innovative signal processing and machine learning methods, we will identify a wide range of resuscitation quality characteristics over resuscitation, their relation to individual patient characteristics and predictability of OHCA outcomes. These goals will be accomplished via the following aims: Aim 1. Determine the influence of resuscitation interventions on real-time physiologic dynamics and outcomes in OHCA. Aim 2. Establish the influence of individual patient characteristics on the real-time physiologic dynamics and OHCA outcomes. Aim 3. Develop a novel cardiac arrest resuscitation strategy based upon real-time individualized physiologic dynamics. We will create a large repository of cardiopulmonary resuscitation process data encompassing data from over 5300 adult OHCA. This work will define intra-arrest ETCO2 dynamics over resuscitation to allow for the development of guided resuscitation efforts, and the resultant data will provide a solid foundation for future hypothesis-driven research. Dr. Nassal’s training plan encompasses both formal didactics and experiential training with experienced mentors and collaborators that will develop a skillset in both signal processing and equitable artificial intelligent driven algorithms. The team has extensive experience in using machine learning and multimodal signal processing for classification and predictions in resuscitation. This training program will develop a unique skillset in advanced cardiac signal processing; artificial intelligence, including equitable machine learning processing; and expertise in the application of these skills to develop dynamically guided resuscitation strategies that few other physician-scientist possess.