Page 207 - Artificial Intelligence for Computational Modeling of the Heart

Chapter 5 Machine learning methods for robust parameter estimation 179

Figure 5.9. Goodness of fit (volume and pressure curves) after personalization of
an example patient based on the different WBC setups. Additional objectives per
setup are highlighted in bold. With increasing number of parameters and
objectives, the proposed method manages to improve the fit between model and
data.

Figure 5.10. WBC personalization results (top: success rate, bottom: average
number of forward model runs until convergence) for the different setups. Left:
RL-based method performance over increasing number of training data
(cross-validated personalization of all 48 datasets). Right: Performance of
reference method. Each shade represents 10% of the results, sorted by
performance; darkest shade: best 10%.