Digital twins and hybrid modelling for simulation of physiological variables and stroke risk
One of the more interesting ideas for achieving personalized, preventive, and participatory medicine is the concept of a digital twin. A digital twin is a personalized computer model of a patient. So far, digital twins have been constructed using either mechanistic models, which can simulate the trajectory of physiological and biochemical processes in a person, or using machine learning models, which for example can be used to estimate the risk of having a stroke given a cross-section profile at a given timepoint. These two modelling approaches have complementary strengths which can be combined into a hybrid model. However, even though hybrid modelling combining mechanistic modelling and machine learning have been proposed, there are few, if any, real examples of hybrid digital twins available. We now present such a hybrid model for the simulation of ischemic stroke. On the mechanistic side, we develop a new model for blood pressure and integrate this with an existing multi-level and multi-timescale model for the development of type 2 diabetes. This mechanistic model can simulate the evolution of known physiological risk factors (such as weight, diabetes development, and blood pressure) through time, under different intervention scenarios, involving a change in diet, exercise, and certain medications. These forecast trajectories of the physiological risk factors are then used by a machine learning model to calculate the 5-year risk of stroke, which thus also can be calculated for each timepoint in the simulated scenarios. We discuss and illustrate practical issues with clinical implementation, such as data gathering and harmonization. By improving patients understanding of their body and health, the digital twin can serve as a valuable tool for patient education and as a conversation aid during the clinical encounter. As such, it can facilitate shared decision-making, promote behavior change towards a healthy lifestyle, and improve adherence to prescribed medications.