  Cardiocirculatory mathematical models serve as valuable tools for
investigating physiological and pathological conditions of the circulatory
system. To investigate the clinical condition of an individual,
cardiocirculatory models need to be personalized by means of calibration
methods. In this study we propose a new calibration method for a
lumped-parameter cardiocirculatory model. This calibration method utilizes the
correlation matrix between parameters and model outputs to calibrate the latter
according to data. We test this calibration method and its combination with
L-BFGS-B (Limited memory Broyden - Fletcher - Goldfarb - Shanno with Bound
constraints) comparing them with the performances of L-BFGS-B alone. We show
that the correlation matrix calibration method and the combined one effectively
reduce the loss function of the associated optimization problem. In the case of
in silico generated data, we show that the two new calibration methods are
robust with respect to the initial guess of parameters and to the presence of
noise in the data. Notably, the correlation matrix calibration method achieves
the best results in estimating the parameters in the case of noisy data and it
is faster than the combined calibration method and L-BFGS-B. Finally, we
present real test case where the two new calibration methods yield results
comparable to those obtained using L-BFGS-B in terms of minimizing the loss
function and estimating the clinical data. This highlights the effectiveness of
the new calibration methods for clinical applications.
