Control of Medical Digital Twins with Artificial Neural Networks
<|reference_start|>Control of Medical Digital Twins with Artificial Neural Networks: 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.<|reference_end|>
@article{böttcher2024control,
    title={Control of Medical Digital Twins with Artificial Neural Networks},
    author={Lucas B"ottcher, Luis L. Fonseca, Reinhard C. Laubenbacher},
    journal={arXiv preprint arXiv:2403.13851},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13851},
    primaryClass={q-bio.QM cs.LG cs.SY eess.SY math.DS math.OC}
}