A computer-implemented method of determining blood flow characteristics of a patient, the method comprising: receiving, in an electronic storage medium, patient-specific image data of at least a portion of vasculature of the patient having geometric features at one or more points; generating a patient-specific reduced order model from the received image data, the patient-specific reduced order model comprising estimates of impedance values and a simplification of the geometric features at the one or more points of the vasculature of the patient; determining blood flow characteristics at the one or more points of the patient's vasculature using a first machine learning algorithm trained to predict blood flow characteristics; the first machine learning algorithm being trained using a first training data set comprising first feature vectors comprising geometric features of at least one 3D anatomic model of said vasculature, the geometric features being determined at one or more points of the at least one 3D anatomic model, the first training set further comprising blood flow characteristics associated with said first feature vectors at said one or more points of the at least one 3D anatomic model; characterized in that: the method comprises creating, for one or more points of the patient specific reduced order model, second feature vectors; and the determining of blood flow characteristics is based on determining blood flow characteristics at the one or more points of the patient-specific reduced order model by using said trained first machine learning algorithm with said second feature vectors created for the reduced-order model. receiving, in an electronic storage medium, patient-specific image data of at least a portion of vasculature of the patient having geometric features at one or more points; generating a patient-specific reduced order model from the received image data, the patient-specific reduced order model comprising estimates of impedance values and a simplification of the geometric features at the one or more points of the vasculature of the patient; determining blood flow characteristics at the one or more points of the patient's vasculature using a first machine learning algorithm trained to predict blood flow characteristics; the first machine learning algorithm being trained using a first training data set comprising first feature vectors comprising geometric features of at least one 3D anatomic model of said vasculature, the geometric features being determined at one or more points of the at least one 3D anatomic model, the first training set further comprising blood flow characteristics associated with said first feature vectors at said one or more points of the at least one 3D anatomic model; characterized in that: the method comprises creating, for one or more points of the patient specific reduced order model, second feature vectors; and the determining of blood flow characteristics is based on determining blood flow characteristics at the one or more points of the patient-specific reduced order model by using said trained first machine learning algorithm with said second feature vectors created for the reduced-order model. the method comprises creating, for one or more points of the patient specific reduced order model, second feature vectors; and the determining of blood flow characteristics is based on determining blood flow characteristics at the one or more points of the patient-specific reduced order model by using said trained first machine learning algorithm with said second feature vectors created for the reduced-order model.