Bayesian Parameter Inference and Uncertainty Quantification for a Computational Pulmonary Hemodynamics Model Using Gaussian Processes
Journal:
arXiv
Published Date:
Feb 20, 2025
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.
