Knapp Adam C, Cruz Daniel A, Mehrad Borna, Laubenbacher Reinhard C
Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Florida.
bioRxiv. 2024 Nov 7:2024.05.31.596692. doi: 10.1101/2024.05.31.596692.
Digital twin technology, pioneered for engineering applications, is being adapted to biomedicine and healthcare; however, several problems need to be solved in the process. One major problem is that of dynamically calibrating a computational model to an individual patient, using data collected from that patient over time. This kind of calibration is crucial for improving model-based forecasts and realizing personalized medicine. The underlying computational model often focuses on a particular part of human biology, combines different modeling paradigms at different scales, and is both stochastic and spatially heterogeneous. A commonly used modeling framework is that of an agent-based model, a computational model for simulating autonomous agents such as cells, which captures how system-level properties are affected by local interactions. There are no standard personalization methods that can be readily applied to such models. The key challenge for any such algorithm is to bridge the gap between the clinically measurable quantities (the macrostate) and the fine-grained data at different physiological scales which are required to run the model (the microstate). In this paper we develop an algorithm which applies a classic data assimilation technique, the ensemble Kalman filter, at the macrostate level. We then link the Kalman update at the macrostate level to an update at the microstate level that produces microstates which are not only compatible with desired macrostates but also highly likely with respect to model dynamics.
数字孪生技术起源于工程应用领域，目前正被应用于生物医学和医疗保健领域；然而，在此过程中需要解决几个问题。一个主要问题是如何利用从个体患者随时间收集的数据，将计算模型动态校准到该个体患者身上。这种校准对于改进基于模型的预测和实现个性化医疗至关重要。底层的计算模型通常聚焦于人类生物学的特定部分，在不同尺度上结合了不同的建模范式，并且具有随机性和空间异质性。一种常用的建模框架是基于主体的模型，这是一种用于模拟细胞等自主主体的计算模型，它能够捕捉局部相互作用如何影响系统层面的特性。目前还没有可以直接应用于此类模型的标准个性化方法。任何此类算法面临的关键挑战是弥合临床可测量量（宏观状态）与运行模型所需的不同生理尺度上的细粒度数据（微观状态）之间的差距。在本文中，我们开发了一种算法，该算法在宏观状态层面应用了一种经典的数据同化技术——集合卡尔曼滤波器。然后，我们将宏观状态层面的卡尔曼更新与微观状态层面的更新联系起来，这种微观状态更新不仅能产生与期望宏观状态兼容的微观状态，而且在模型动力学方面具有很高的可能性。
