The objective of personalized medicine is to tailor interventions to an
individual patient's unique characteristics. A key technology for this purpose
involves medical digital twins, computational models of human biology that can
be personalized and dynamically updated to incorporate patient-specific data
collected over time. Certain aspects of human biology, such as the immune
system, are not easily captured with physics-based models, such as differential
equations. Instead, they are often multi-scale, stochastic, and hybrid. This
poses a challenge to existing model-based control and optimization approaches
that cannot be readily applied to such models. Recent advances in automatic
differentiation and neural-network control methods hold promise in addressing
complex control problems. However, the application of these approaches to
biomedical systems is still in its early stages. This work introduces
dynamics-informed neural-network controllers as an alternative approach to
control of medical digital twins. As a first use case for this method, the
focus is on agent-based models, a versatile and increasingly common modeling
platform in biomedicine. The effectiveness of the proposed neural-network
control method is illustrated and benchmarked against other methods with two
widely-used agent-based model types. The relevance of the method introduced
here extends beyond medical digital twins to other complex dynamical systems.

个性化医学的目标是根据患者的独特特征量身定制干预措施。本研究介绍了一种基于动态信息的神经网络控制器作为对医学数字孪生的控制方法，并通过在两种常用的基于代理的模型类型上进行实验和评估验证了该方法的有效性。