Bayesian Parameter Inference and Uncertainty Quantification for a Computational Pulmonary Hemodynamics Model Using Gaussian Processes.

Abstract

Patient-specific modeling is a valuable tool in cardiovascular disease research, offering insights beyond what current clinical equipment can measure. Given the limitations of available clinical data, models that incorporate uncertainty can provide clinicians with better guidance for tailored treatments. However, such modeling must align with clinical time frameworks to ensure practical applicability. In this study, we employ a one-dimensional fluid dynamics model integrated with data from a canine model of chronic thromboembolic pulmonary hypertension (CTEPH) to investigate microvascular disease, which is believed to involve complex mechanisms. To enhance computational efficiency during model calibration, we implement a Gaussian process emulator. This approach enables us to explore the relationship between disease severity and microvascular parameters, offering new insights into the progression and treatment of CTEPH in a timeframe that is compatible with a reasonable clinical timeframe.