CAREER: Bayesian Inference Networks for Model Ensembles

将心血管模型、专家意见和临床测量整合到一个连贯的系统中进行概率推理，代表了模型辅助诊断的下一个前沿领域，为个性化医疗的治疗提供信息。然而，必须解决几个基本的限制：(1)确定性模型不足以表征心血管系统各种特性的不确定性影响；(2)心血管模型在计算上是昂贵的，因此对这些模型进行随机处理可能在计算上过于昂贵；(3)在复杂的决策工作流程中需要预测模型，以合理的概率方式直接回答临床相关的问题。该项目提出了三个互补领域的关键进展，这些领域对于使下一代模型辅助诊断成为现实至关重要：(1)高度可扩展的计算方法能够有效地处理具有不确定参数的心血管模型的多个实例；(2)利用计算成本低廉的低保真替代物的新型蒙特卡罗估计器；(3)基于网络组织变量的新型推理系统，可容纳非线性血流动力学模型、专家意见和数据。由于心血管疾病是世界范围内死亡的主要原因，该项目通过推进国家健康、繁荣和福利来服务于国家利益，正如NSF的使命所述。在计算数学和生理学的界面上提出的研究为在高中、本科和研究生阶段培养多样化和具有全球竞争力的STEM劳动力提供了一个理想的框架。研讨会和小型专题讨论会也将促进科学界关于随机心血管建模的思想交流。计算模型越来越多地被用于个性化医疗的治疗，但基于模型的诊断的创新仍然受到三个主要问题的阻碍：(1)确定性模拟，即具有特定输出的模拟提供了错误的信心感；(2)随机模拟通常伴随着计算成本的急剧增加；(3)当前的不确定性量化范式需要推广，以提供结合物理模型、专家意见、观测数据和实验的连贯推理框架。因此，本CAREER提案的主要目标是开发下一代高效的计算工具，以加速计算血流动力学模型和数据的推断以及广泛的应用。这一目标是通过以下目标实现的：(1)开发在现代CPU/GPU混合架构上运行的高效集成求解器；(2)研究广义近似控制变量蒙特卡罗估计，以大大减少求解不确定性分析中的正逆问题所需的时间；(3)将数值模型、专家意见和数据结合在一个连贯的推理框架中扩展贝叶斯网络。本研究将为构建首个包括实验证据、专家意见在内的基于模型的推理框架，开发直接应用于临床决策过程的系统提供科学依据。最终将开发两个原型系统，重点是应用于儿科手术。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。

Integration of cardiovascular models, expert opinion and clinical measurements in a coherent system for probabilistic reasoning represents the next frontier of model-aided diagnostics to inform treatment in personalized medicine. However, several fundamental limitations must be addressed: (1) deterministic models are inadequate to characterize the effect of uncertainty in various properties of the cardiovascular system; (2) cardiovascular models are computationally expensive and therefore a stochastic treatment of these models may be too computationally expensive; (3) predictive models are needed in complex decision workflows to directly answer questions of clinical relevance in justifiable, probabilistic terms. This project proposes key advances in three complementary areas that are essential to make this next generation of model-aided diagnostics a reality: (1) highly scalable computational methods able to efficiently handle multiple instances of cardiovascular models with uncertain parameters; (2) new Monte Carlo estimators that leverage computationally inexpensive low fidelity surrogates; (3) new inference systems based on variables organized in networks, accommodating non-linear hemodynamic models, expert opinion and data. Since cardiovascular disease is the leading causes of death worldwide, this project serves the national interest by advancing the national health, prosperity and welfare, as stated by NSF's mission. The proposed research at the interface of computational mathematics and physiology offers an ideal framework to educate a diverse and globally competitive STEM workforce at the high school, undergraduate and graduate levels. Workshops and mini-symposia will also facilitate the exchange of ideas on stochastic cardiovascular modeling within the scientific community. Computational models are increasingly being adopted to inform treatment in personalized medicine but innovation in model-based diagnostics is still hindered by three main problems: (1) deterministic simulations, i.e., simulations with certain outputs providing a false sense of confidence; (2) stochastic simulations are typically associated with a dramatic increase in computational cost; (3) current paradigms in uncertainty quantification (UQ) need generalization to provide coherent inference frameworks combining physics-based models, expert opinion, observational data and experiments. Thus, the main goal of this CAREER proposal is to develop the next generation of efficient computational tools to accelerate inference from models and data in computational hemodynamics as well as in a wide range of applications. This goal is achieved through the following objectives: (1) development of efficient ensemble solvers for hemodynamics running on modern CPU/GPU hybrid architectures; (2) research in generalized approximate control variate Monte Carlo estimators to drastically reduce the time required to solve direct and inverse problems in uncertainty analysis; (3) extend Bayesian Networks combining numerical models, expert opinion and data in a coherent inference framework. This research will provide the scientific basis to construct the first model-based inference framework including experimental evidence, expert opinion, and to develop systems directly applicable to the clinical decision making process. Two prototype systems will be finally developed, with a focus on applications to pediatric surgery.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.