Publication
Systems 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.
Nathan Quentin Faivre, Inaki Asier Iturrate Gil, Michael Eric Anthony Pereira, Xiao Hu, Caroline Peters