Figure 1.

(a) A two‐parameter example illustrates the method of using machine learning (ML) to improve efficiency of global sensitivity analysis for complex mathematical models. Taking a random sample of model parameters a and b, simulations are performed and the outcomes recorded. The parameter sample and corresponding outcomes are used to train an ML algorithm, which is subsequently used to predict model outcomes for a richer range of parameter sets in order to streamline model analyses. (b) The workflow illustrates how to integrate big data into modeling and simulation. Data and prior knowledge are used for calibrating the parameters of a mathematical model, depicted by the box “M&S.” The resulting patient‐specific parameters are passed as “output” along with ‐omics and imaging data as “input” to train an ML algorithm, with the aim to establish a link between them. By predicting model parameter sets for measured “input” data, time courses and their variabilities can be analyzed by forward evaluation of the mathematical model.