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

该奖项将通过开发因果AI数字孪生框架，支持关键护理服务，促进“医疗保健4.0”的实现，为国民健康和福利的进步做出贡献。败血症和肺炎引起的重症是院内死亡的主要原因，也是全球卫生重点。虽然早期诊断和无差错治疗始终能取得良好的结果，但危重疾病进展为多器官衰竭往往导致死亡或丧失独立性。COVID-19大流行暴露了世界各地医院在重症监护知识和实践方面长期存在的缺陷。美国国家医学院呼吁对临床医学采用一种新的系统科学方法，并需要新的方法和策略来促进及时和准确的干预。该项目将为重症监护病房（ICU）提供有价值的解决方案，开发一个虚拟的重症监护病房（ICU），并为其提供决策支持，以便在多个层面上为患者健康和护理提供信息。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，一群不同的学生和临床研究员将接受机器学习、系统工程和重症监护医学方面的跨学科培训。这种因果人工智能数字孪生框架将是支持更有效的医学教育和最终减少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个综合任务来解决重症监护服务中的挑战：(1)学习一个支持重症患者临床路径的因果AI模型，(2)研究危重患者在最初24小时内的最佳治疗决策，以及(3)通过数字孪生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用因果贝叶斯网络表示。将开发计算效率高的方法来学习给定高维和不可观察变量的网络。将开发强化学习方法，以在患者护理的早期阶段对个体患者进行最佳治疗。患者安全系统工程倡议（SEIPS） 2.0模型将适用于ICU系统，以确定影响重症护理交付的主要因素。最后，为了分析重症护理交付的患者级和过程级交互，将研究基于agent的仿真和离散事件仿真之间的混合结构和设计，从而实现可靠的混合仿真模型。这项研究的应用有望使ICU数字双胞胎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关键护理交付的最佳策略，从而降低现实生活中患者的风险。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.