HPC Optimization Algorithm for Assessing Haemodynamic Parameters in Synthetic Patient Cohorts

A computational framework for the generation of a synthetic pulse wave database is developed. This framework demonstrates the feasibility of generating large-scale, high-fidelity virtual patient cohorts for biomedical research. Pulse waves are generated using a one-dimensional hemodynamic model of the systemic circulation coupled with a model of the left heart. Each virtual patient in the database is defined by a set of physiological parameters, including systolic and diastolic blood pressure, stroke volume, and heart rate. The parameters are optimized to match the desired outputs by solving an inverse problem using the Unscented Kalman Filter (UKF). The UKF is selected for its ability to accurately and efficiently estimate parameters in nonlinear systems. The generation of a single virtual patient requires between one and several hundred iterations of the UKF, depending on the complexity of the desired outputs. To meet the computational demands of generating a database with thousands of virtual patients, a computing cluster with 24 CPU nodes, each containing 52 cores, is employed. Two levels of parallelization are implemented, resulting in a speedup factor of 8.