Utilizing high resolution physiological data and artificial intelligence to develop a pediatric cardiac arrest prediction tool for integration into bedside clinical practice

利用高分辨率生理数据和人工智能开发儿科心脏骤停预测工具，以融入床边临床实践

• 批准号: 538815-2019
• 负责人: Goldenberg, Anna
• 金额: $ 5.98万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Health Research Projects
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=692275
• 关键词: Utilizing; resolution; physiological; data; artificial

Critically ill children are at risk of cardio-pulmonary arrest (CPA). These devastating events occur 300-700 times per year in Canadian pediatric intensive care units (PICUs) and are associated with high mortality (45 to 85%), disability and higher costs of care. Effective management of CPA requires immediate reaction from clinicians. However, changes in apatients state that precede the CPA are subtle. If these were appreciated, interventions would be possible, potentially preventing CPA and the associated poor outcomes and costs. The objective of this project is to use artificial intelligence to develop and implement a tool to predict CPA in critically ill children. We have been collecting and storing continuous physiologic signals in our PICU since April 2013. We have undertaken preliminary work using machine learning to analyze these data in patients who suffered a CPA. Our results indicate that earlier prediction of CPA is possible.We will use funding from this grant to: 1) Further validate, train and test the model predicting CPA, utilizing multidisciplinary expertise in a team that includes clinicians, engineers, computer scientists, and 2) Translate the model to the bedside using a visualization platform and implementation plan developed with human factors engineers and ethicists. This project is part of the new field of predictive, personalized medicine, and is associated with novel ethical dilemmas related to communicating the significance of predictions to patients and their families, which we will explore and define. An accurate model for CPA will save the lives of critically ill Canadian children and reduce the burden associated with these events. Its development will create opportunities for Canadian researchers to extend predictive modelling and redefine the healthcare team. This initiativewill reduce healthcare costs, and positions Canada as a leader in using artificial intelligence to solve crucial healthcare problems.

危重儿童有心肺骤停(CPA)的风险。这些毁灭性的事件每年在加拿大儿科重症监护病房(PICU)发生300-700次，并与高死亡率(45%至85%)、残疾和更高的护理成本有关。对CPA的有效管理需要临床医生立即做出反应。然而，在CPA之前，病人状态的变化是微妙的。如果认识到这一点，就有可能采取干预措施，潜在地防止CPA以及相关的不良结果和成本。本项目的目标是利用人工智能开发和实施一种工具来预测危重儿童的CPA。自2013年4月以来，我们一直在我们的PICU中收集和存储连续的生理信号。我们已经开展了初步工作，使用机器学习来分析遭受CPA的患者的这些数据。我们的结果表明，CPA的早期预测是可能的。我们将利用这笔赠款的资金：1)利用包括临床医生、工程师、计算机科学家在内的团队中的多学科专业知识，进一步验证、训练和测试CPA预测模型；2)使用与人为因素工程师和伦理学家共同开发的可视化平台和实施计划，将模型转换到床边。这个项目是新的预测性、个性化医学领域的一部分，与我们将探索和定义的与向患者及其家人传达预测的意义相关的新的伦理困境有关。一个准确的CPA模型将拯救加拿大危重儿童的生命，并减轻与这些事件相关的负担。它的发展将为加拿大研究人员提供扩展预测建模和重新定义医疗团队的机会。这一举措将降低医疗成本，并使加拿大在使用人工智能解决关键医疗问题方面处于领先地位。