Collaborative Research: SCH: A Causal AI Digital Twin Framework to Transform Intensive Care Delivery

这一智能与互联健康(SCH)奖将通过开发因果AI数字孪生框架来支持危重护理服务的提供并促进“Healthcare 4.0”的实现，从而为促进国家健康和福利的进步做出贡献。败血症和肺炎引发的危重疾病是院内死亡的主要原因，也是全球卫生优先事项。虽然早期诊断和无差错治疗一直都能获得良好的结果，但危重疾病发展到多器官衰竭往往意味着死亡或丧失独立性。新冠肺炎疫情暴露了全球医院在危重护理知识和实践方面长期存在的不足。美国国家医学科学院呼吁对临床医学采取新的系统科学方法，需要新的方法和战略来促进及时和准确的干预。该项目将通过开发与重症监护病房(ICU)相对应的虚拟解决方案来提供有价值的重症监护服务，并提供决策支持，以在多个层面上向患者提供健康和护理服务。一个由工程师、科学家和临床专业人员组成的多学科团队已经建立了持续的、成功的合作，并将致力于这项研究。此外，通过这项研究，一批不同的学生和临床研究员将接受机器学习、系统工程和重症护理医学方面的跨学科培训。这一因果人工智能数字双胞胎框架将是支持更有效的医学教育和最终不容易出错的床边决策的关键飞跃。这项合作研究的目标是通过三项综合任务解决重症护理提供方面的挑战：(1)学习支撑危重患者临床路径的因果人工智能模型，(2)调查危重患者在最初24小时内的最佳治疗决策，以及(3)通过数字双胞胎框架实现系统级干预。在专家知识的支持下，重症脓毒症患者的临床路径将由因果贝叶斯网络表示。在给定高维和不可观测变量的情况下，将开发出计算高效的方法来学习网络。将开发强化学习方法，以研究在患者护理的早期阶段对个别患者的最佳治疗。患者安全系统工程倡议(SEIPS)2.0模式将适用于ICU系统，以确定影响危重护理提供的主要因素。最后，为了能够分析重症护理交付的患者级和流程级交互作用，将研究基于代理的模拟和离散事件模拟的混合结构和设计，从而实现可靠的混合模拟模型。这项研究的应用预计将使ICU数字孪生平台能够支持床边临床医生、教育工作者和医院管理人员选择提供危重护理的最佳策略，从而降低现实生活中患者的风险。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Smart and Connected Health (SCH) award will contribute to the advancement of the national health and welfare by developing a causal AI digital twin framework to support critical care delivery and facilitate the realization of “Healthcare 4.0.” Critical illness from sepsis and pneumonia is the leading cause of in-hospital mortality and a global health priority. While early diagnosis and error-free treatment consistently achieve good outcomes, the progression of critical illness to multiple organ failure often translates to either death or loss of independence. The COVID-19 pandemics have exposed long standing deficiencies in critical care knowledge and practice in hospitals worldwide. The National Academy of Medicine has called for a novel systems science approach to clinical medicine and new methods and strategies to facilitate timely and accurate interventions are needed. This project will provide valuable solutions to critical care delivery by developing a virtual counterpart to the intensive care unit (ICU) bolstered with decision support to inform patient health and care delivery at multiple levels. A multidisciplinary team with engineers, scientists, and clinical professionals has established an ongoing, successful collaboration, and will be committed to this research. In addition, through this research, a diverse group of students and clinical fellows will receive a blend of interdisciplinary training in machine learning, systems engineering, and critical care medicine. This causal AI digital twin framework will be a critical leap forward in support of a more efficient medical education and eventually less error-prone bedside decision making.The goal of this collaborative research is to tackle the challenges in critical care delivery through three integrated tasks: (1) learning a causal AI model underpinning the clinical pathway of critically ill patients, (2) investigation of optimal treatment decisions for critically ill patients in the first 24 hours, and (3) enabling system-level interventions through a digital twin framework. Supported by expert knowledge, the clinical pathway of critically ill sepsis patients will be represented by causal Bayesian networks. Computationally efficient approaches will be developed to learn the networks given high-dimensional and unobservable variables. Reinforcement learning approaches will be developed to investigate the optimal treatment for individual patients in the early stage of patient care. The Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model will be adapted to the ICU system to identify the principal factors affecting critical care delivery. Lastly, to enable the analysis of the patient-level and the process-level interactions of critical care delivery, the hybridization structure and design between agent-based simulation and discrete-event simulation will be investigated, thereby achieving a reliable hybrid simulation model. The application of this research is expected to enable an ICU digital twin platform that supports the bedside clinicians, educators, and hospital administrators to choose optimal strategies for critical care delivery, thereby mitigating risk of real-life patients.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.