2017
DOI: 10.1007/978-3-319-59050-9_18
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Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models

Abstract: Estimation of patient-specific model parameters is important for personalized modeling, although sparse and noisy clinical data can introduce significant uncertainty in the estimated parameter values. This importance source of uncertainty, if left unquantified, will lead to unknown variability in model outputs that hinder their reliable adoptions. Probabilistic estimation model parameters, however, remains an unresolved challenge because standard Markov Chain Monte Carlo sampling requires repeated model simula… Show more

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Cited by 5 publications
(4 citation statements)
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“…This study extends our previous work in Dhamala et al (2017b) in the following primary respects: A theoretical examination on the convergence property of the presented GP-accelerated MH method.An evaluation and generalization of the presented method on two types of low-dimensional representations of the parameter space, and a comparative analysis of the resulting posterior pdfs to understand how uncertainty of the patient-specific solutions varies with the low-dimensional representation of choice.An evaluation of the presented framework in the presence of highly heterogeneous tissue properties, using measurement data generated from an EP model blinded to the presented framework and validation data of myocardial scar obtained from in-vivo magnetic resonance images.A comprehensive analysis of different factors that contribute to the uncertainty in the obtained patient-specific model parameters.…”
Section: Introductionsupporting
confidence: 92%
“…This study extends our previous work in Dhamala et al (2017b) in the following primary respects: A theoretical examination on the convergence property of the presented GP-accelerated MH method.An evaluation and generalization of the presented method on two types of low-dimensional representations of the parameter space, and a comparative analysis of the resulting posterior pdfs to understand how uncertainty of the patient-specific solutions varies with the low-dimensional representation of choice.An evaluation of the presented framework in the presence of highly heterogeneous tissue properties, using measurement data generated from an EP model blinded to the presented framework and validation data of myocardial scar obtained from in-vivo magnetic resonance images.A comprehensive analysis of different factors that contribute to the uncertainty in the obtained patient-specific model parameters.…”
Section: Introductionsupporting
confidence: 92%
“…We first evaluated the accuracy and efficiency of the presented method against 1) directly sampling GP approximation of the posterior pdf based on regular Bayesian active learning and 2) surrogate-accelerated two-stage MCMC sampling as presented in our previous work (Dhamala et al, 2017b ), all against the baseline of directly sampling the exact posterior pdf using the standard MCMC. We considered 15 synthetic cases in total.…”
Section: Experiments and Resultsmentioning
confidence: 99%
“…In addition, it has been shown that the initialization of model parameters becomes increasingly more critical as the number of segments grows [17]. In an alternative approach, the explicit partitioning of the cardiac mesh is done through a coarse-to-fine optimization along a pre-defined multi-scale hierarchy of the cardiac mesh, enabling spatially-adaptive resolution of tissue properties that is higher in certain regions than the others [4,6,7,5]. However, the representation ability of the final partition is limited by the inflexibility of the pre-defined multiscale hierarchy: homogeneous regions distributed across different scales cannot be grouped into the same partition, while the resolution of heterogeneous regions can be limited by the level of the scale the optimization can reach [6].…”
Section: Introductionmentioning
confidence: 99%