Bikia, VasilikiPagoulatou, StamatiaStergiopulos, Nikolaos2021-03-012021-03-012021-03-012021https://infoscience.epfl.ch/handle/20.500.14299/175585Systems and methods are provided for that use noninvasively measured physiologic parameters to predict in real time noninvasively unobservable cardiovascular parameters by employing a one-dimensional arterial tree numerical model calibrated with representative patient data. The numerical model further may be trained and calibrated on a larger database that includes synthetic data using machine-learning algorithms to provide a robust generalized estimator for multiple cardiovascular and hemodynamic parameters.patentUS2022287640WO202103309772193508