Metamodeling and Control of Medical Digital Twins
CoRR(2024)
摘要
The vision of personalized medicine is to identify interventions that
maintain or restore a person's health based on their individual biology.
Medical digital twins, computational models that integrate a wide range of
health-related data about a person and can be dynamically updated, are a key
technology that can help guide medical decisions. Such medical digital twin
models can be high-dimensional, multi-scale, and stochastic. To be practical
for healthcare applications, they need to be simplified into low-dimensional
metamodels that can be used for forecasting and optimal design of
interventions. This paper introduces metamodeling algorithms for the purpose of
optimal control applications. It uses agent-based models as a use case, a
common model type in biomedicine for which there are no readily available
optimal control algorithms. With systems of ordinary differential equations as
metamodels, optimal control methods can be applied to the metamodels, and
results can be lifted to the agent-based model.
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