Improving personalized tumor growth predictions using a Bayesian combination of mechanistic modeling and machine learning.

We show that the combination of machine learning and mathematical modeling approaches can lead to accurate predictions of clinical outputs in the context of data sparsity and limited knowledge of disease mechanisms.