Artificial Intelligence for Dynamic, individualized CPR guidance: AID CPR

人工智能提供动态、个性化的心肺复苏指导：AID CPR

• 批准号: 10644648
• 负责人: Michelle M.J. Nassal
• 金额: $ 16.35万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10644648
• 关键词: Adult; Affect; Algorithms; American; Artificial Intelligence; Automobile Driving; Benchmarking; Biological Markers; Biometry; Canada; Capnography; Carbon Dioxide; Cardiac; Cardiopulmonary Resuscitation; Caring; Characteristics; Circulation; Classification; Collaborations; Collection; Communities; Country; Data; Data Set; Development; Disparity; Effectiveness of Interventions; Emergency medical service; Epinephrine; Equity; Ethnic Origin; Etiology; Feedback; Foundations; Functional disorder; Future; Gender; Goals; Grant; Healthcare; Heart Arrest; Hospitals; Hypoventilation; Individual; International; Intervention; Machine Learning; Measurement; Measures; Medical center; Medicine; Mentors; Modeling; Ohio; Outcome; Patient Care; Patients; Perfusion; Persons; Pharmaceutical Preparations; Physicians; Physiological; Physiology; Process; Public Health; Race; Research; Research Personnel; Resuscitation; Scientist; Sex Bias; Solid; Spain; Technology; Testing; Texas; Time; Training; Training Programs; Universities; Variant; Work; ethnic bias; experience; heart rhythm; human data; improved; improved outcome; individual patient; individualized medicine; innovation; insight; machine learning method; multimodality; novel; out-of-hospital cardiac arrest; personalized approach; racial bias; repository; signal processing; simulation; skills; success; time interval; tool; ventilation

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.

项目总结/摘要 院外心脏骤停（OHCA）是一个动态过程，需要新的干预措施来改善 结果。呼气末二氧化碳（ETCO 2）测量是一种被广泛认可的工具，易于使用， 可以潜在地提供对正在进行的复苏工作的实时洞察;然而，它还没有被应用于 个性化医疗我们的总体假设是，将ETCO 2二氧化碳描记术整合到OHCA复苏中 将改善结果。使用创新的信号处理和机器学习方法，我们将识别广泛的 复苏质量特征的范围，其与个体患者特征的关系 OHCA结果的可预测性。 这些目标将通过以下目标实现： 目标1.确定复苏干预对实时生理动力学和结局的影响 在OHCA。 目标二。确定个体患者特征对实时生理动力学的影响， OHCA结果。 目标3。基于实时个体化生理参数制定新型心脏骤停复苏策略 动力学 我们将创建一个大型的心肺复苏过程数据库， 5300成人OHCA。这项工作将定义复苏期间的停搏内ETCO 2动态，以允许 引导复苏工作的发展，以及由此产生的数据将为未来的复苏提供坚实的基础。 假设驱动的研究。 Nassal博士的培训计划包括正式的教学法和经验丰富的体验式培训。 导师和合作者，将开发在信号处理和公平的人工智能的技能 驱动算法该团队在使用机器学习和多模态信号方面拥有丰富的经验 用于复苏中的分类和预测的处理。这项培训计划将培养一种独特的技能 先进的心脏信号处理;人工智能，包括公平的机器学习处理; 以及应用这些技能开发动态引导复苏策略的专业知识， 其他的物理学家和科学家拥有的。