Surrogate Modeling and Control of Medical Digital Twins

TL;DR

This paper develops surrogate modeling algorithms to enable optimal control of complex medical digital twins, specifically using agent-based models, facilitating personalized medical interventions.

Contribution

It introduces a novel surrogate modeling approach for agent-based models, allowing the application of optimal control methods in personalized medicine.

Findings

Surrogate models based on ODE systems enable effective control of ABMs.

Optimal control algorithms can be applied to complex digital twin models.

The methods are applicable beyond biomedical systems.

Abstract

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 often need to be simplified into low-dimensional surrogate models that can be used for the optimal design of interventions. This paper introduces surrogate modeling algorithms for the purpose of optimal control applications. As a use case, we focus on agent-based models (ABMs), a common model type in biomedicine for which there are no readily available optimal control algorithms. By deriving surrogate models that are based on systems of ordinary differential equations, we show how optimal control methods can be employed to compute effective interventions, which can then be lifted back to a given ABM. The relevance of the methods introduced here extends beyond medical digital twins to other complex dynamical systems.