  Personalization is the process of fitting a model to patient data, a critical
step towards application of multi-physics computational models in clinical
practice. Designing robust personalization algorithms is often a tedious,
time-consuming, model- and data-specific process. We propose to use artificial
intelligence concepts to learn this task, inspired by how human experts
manually perform it. The problem is reformulated in terms of reinforcement
learning. In an off-line phase, Vito, our self-taught artificial agent, learns
a representative decision process model through exploration of the
computational model: it learns how the model behaves under change of
parameters. The agent then automatically learns an optimal strategy for on-line
personalization. The algorithm is model-independent; applying it to a new model
requires only adjusting few hyper-parameters of the agent and defining the
observations to match. The full knowledge of the model itself is not required.
Vito was tested in a synthetic scenario, showing that it could learn how to
optimize cost functions generically. Then Vito was applied to the inverse
problem of cardiac electrophysiology and the personalization of a whole-body
circulation model. The obtained results suggested that Vito could achieve
equivalent, if not better goodness of fit than standard methods, while being
more robust (up to 11% higher success rates) and with faster (up to seven
times) convergence rate. Our artificial intelligence approach could thus make
personalization algorithms generalizable and self-adaptable to any patient and
any model.
