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.

翻译：心脏循环数学模型是研究循环系统生理与病理状况的重要工具。为探究个体临床状况，需通过标定方法对心脏循环模型进行个性化。本研究提出一种针对集总参数心脏循环模型的新标定方法。该方法利用参数与模型输出之间的相关性矩阵，根据数据对后者进行标定。我们测试了该标定方法及其与L-BFGS-B（带边界约束的有限内存Broyden-Fletcher-Goldfarb-Shanno算法）的组合，并对比其与单独使用L-BFGS-B的性能表现。研究表明，相关性矩阵标定方法及其组合方法均能有效降低相关优化问题的损失函数。在计算机生成数据的案例中，两种新标定方法对参数初始猜测及数据噪声均表现出鲁棒性。值得注意的是，相关性矩阵标定方法在含噪数据参数估计中取得了最优结果，且其运行速度快于组合标定方法与L-BFGS-B。最后，我们展示了真实测试案例：两种新标定方法在损失函数最小化及临床数据估计方面取得与L-BFGS-B可比的结果。这凸显了新标定方法在临床应用中的有效性。