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Uncertainty quantification and global sensitivity analysis are indispensable for patient‐specific applications of models that enhance diagnosis or aid decision‐making. Variance‐based sensitivity analysis methods, which apportion each fraction of the output uncertainty (variance) to the effects of individual input parameters or their interactions, are considered the gold standard. The variance portions...
Patient‐specific modeling requires model personalization, which can be achieved in an efficient manner by parameter fixing and parameter prioritization. An efficient variance‐based method is using generalized polynomial chaos expansion (gPCE), but it has not been applied in the context of model personalization, nor has it ever been compared with standard variance‐based methods for models with many...
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