Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators

Calibration of cardiac electrophysiology models is a fundamental aspect of model personalization for predicting the outcomes of cardiac therapies, simulation testing of device performance for a range of phenotypes, and for fundamental research into cardiac function. Restitution curves provide information on tissue function and can be measured using clinically feasible measurement protocols. We introduce novel “restitution curve emulators” as probabilistic models for performing model exploration, sensitivity analysis, and Bayesian calibration to noisy data. These emulators are built by decomposing restitution curves using principal component analysis and modeling the resulting coordinates with respect to model parameters using Gaussian processes. Restitution curve emulators can be used to study parameter identifiability via sensitivity analysis of restitution curve components and rapid inference of the posterior distribution of model parameters given noisy measurements. Posterior uncertainty about parameters is critical for making predictions from calibrated models, since many parameter settings can be consistent with measured data and yet produce very different model behaviors under conditions not effectively probed by the measurement protocols. Restitution curve emulators are therefore promising probabilistic tools for calibrating electrophysiology models.

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Groundwater pollution risk using a modified Latin hypercube sampling

Journal of Hydroinformatics ◽

10.2166/hydro.2006.018b ◽

2006 ◽

Vol 8 (3) ◽

pp. 223-234 ◽

Cited By ~ 3

Author(s):

Husam Baalousha

Keyword(s):

Sensitivity Analysis ◽

Groundwater Pollution ◽

Probabilistic Models ◽

Latin Hypercube Sampling ◽

Model Parameters ◽

Latin Hypercube ◽

Deterministic Models ◽

Estimation Errors ◽

Mean Estimation ◽

Longitudinal Dispersivity

Characterisation of groundwater modelling involves significant uncertainty because of estimation errors of these models and other different sources of uncertainty. Deterministic models do not account for uncertainties in model parameters, and thus lead to doubtful output. The main alternatives for deterministic models are the probabilistic models and perturbation methods such as Monte Carlo Simulation (MCS). Unfortunately, these methods have many drawbacks when applied in risk analysis of groundwater pollution. In this paper, a modified Latin Hypercube Sampling method is presented and used for risk, uncertainty, and sensitivity analysis of groundwater pollution. The obtained results were compared with other sampling methods. Results of the proposed method have shown that it can predict the groundwater contamination risk for all values of probability better than other methods, maintaining the accuracy of mean estimation. Sensitivity analysis results reveal that the contaminant concentration is more sensitive to longitudinal dispersivity than to velocity.

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IMPLICATIONS ON RC STRUCTURE PERFORMANCE OF MODEL PARAMETER SENSITIVITY: EFFECT OF CHLORIDES

Journal of Civil Engineering and Management ◽

10.3846/jcem.2010.62 ◽

2010 ◽

Vol 16 (4) ◽

pp. 561-566 ◽

Cited By ~ 9

Author(s):

Rui Miguel Ferreira

Keyword(s):

Sensitivity Analysis ◽

Probabilistic Models ◽

Current Knowledge ◽

Concrete Cover ◽

Model Parameters ◽

Model Parameter ◽

Average Value ◽

Model Code ◽

Chloride Migration ◽

The Impact

This paper presents a sensitivity analysis of the parameters of the model presented in the fib – Model Code for Service Life Design for corrosion initiation due to chloride penetration. In order to adequately apply probabilistic models, a basic understanding of the model and the parameters used and the impact input values for the model parameters have on the outcome is necessary. An erroneous parameter value selection has consequences that are not always appreciated. A sensitivity analysis of the main model parameters was performed by analysing the presented model outcome. The parameter's average value was varied in a range of 20% with respect to the reference average value, whereas keeping the standard deviation constant. The results of the sensitivity analysis show that the reinforcement concrete cover and the ageing exponent of the chloride migration coefficient parameters are those that most influence the outcome of the model and therefore the durability performance of the concrete structure. The least influential model parameters, contrary to what might be expected, are Ccrit and C S,Δx. Furthermore, the importance of environmental temperature on the outcome is noted. The choice of model parameter value is a critical one. Due to the uncertainty of current knowledge with regard to certain parameters caution should be exercised in the use of these models. Santrauka Straipsnyje pristatoma sukurta modelio parametru jautrumo analize – modelio sistema projektuoja korozijos, atsirandančios del chlorido skvarbos, atsiradimo tikimybe. Siekiant tinkamai taikyti tikimybinius modelius būtinas bazinis supratimas apie modelyje naudotu parametru ir ivesties reikšmiu parametru poveikio rezultatus. Klaidingas parametro reikšmes pasirinkimas gali lemti netinkama ivertinima. Pagrindiniu modelio parametru jautrumo analize buvo atlikta tiriant pateikto pavyzdžio rezultatus. Vidutine parametro verte buvo varijuojama 20 % atsižvelgiant i vidutine atskaitos verte ir išlaikant standartinio nuokrypio konstanta. Jautrumo analizes rezultatai rodo, kad armuotos betonines dangos ir chlorido migracijos koeficiento senejimo rodiklio parametrai yra tie, kurie gali labiausiai paveikti modelio rezultatus ir betono struktūros patvarumo charakteristikas. Mažiau itakingi modelio parametrai, priešingai nei būtu galima tiketis, yra Ccrit ir C S,Δx. Aprašyta aplinkos temperatūros itaka rezultatams. Pasirinkti modelio parametru vertes yra labai svarbu. Del dabartiniu žiniu apie tam tikru parametru neapibrežtuma reikia būti atsargiems taikant šiuos modelius.

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A Sensitivity Analysis of the SPACSYS Model

Agriculture ◽

10.3390/agriculture11070624 ◽

2021 ◽

Vol 11 (7) ◽

pp. 624

Author(s):

Yan Shan ◽

Mingbin Huang ◽

Paul Harris ◽

Lianhai Wu

Keyword(s):

Sensitivity Analysis ◽

Wheat Yield ◽

Climatic Conditions ◽

Water Dynamics ◽

Ecological Modelling ◽

Soil Conditions ◽

Model Parameters ◽

Nutrient Inputs ◽

Nutrient Pool ◽

Wheat Field

A sensitivity analysis is critical for determining the relative importance of model parameters to their influence on the simulated outputs from a process-based model. In this study, a sensitivity analysis for the SPACSYS model, first published in Ecological Modelling (Wu, et al., 2007), was conducted with respect to changes in 61 input parameters and their influence on 27 output variables. Parameter sensitivity was conducted in a ‘one at a time’ manner and objectively assessed through a single statistical diagnostic (normalized root mean square deviation) which ranked parameters according to their influence of each output variable in turn. A winter wheat field experiment provided the case study data. Two sets of weather elements to represent different climatic conditions and four different soil types were specified, where results indicated little influence on these specifications for the identification of the most sensitive parameters. Soil conditions and management were found to affect the ranking of parameter sensitivities more strongly than weather conditions for the selected outputs. Parameters related to drainage were strongly influential for simulations of soil water dynamics, yield and biomass of wheat, runoff, and leaching from soil during individual and consecutive growing years. Wheat yield and biomass simulations were sensitive to the ‘ammonium immobilised fraction’ parameter that related to soil mineralization and immobilisation. Simulations of CO2 release from the soil and soil nutrient pool changes were most sensitive to external nutrient inputs and the process of denitrification, mineralization, and decomposition. This study provides important evidence of which SPACSYS parameters require the most care in their specification. Moving forward, this evidence can help direct efficient sampling and lab analyses for increased accuracy of such parameters. Results provide a useful reference for model users on which parameters are most influential for different simulation goals, which in turn provides better informed decision making for farmers and government policy alike.

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Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning

International Journal for Numerical Methods in Biomedical Engineering ◽

10.1002/cnm.3450 ◽

2021 ◽

Author(s):

Stefano Pagani ◽

Andrea Manzoni

Keyword(s):

Machine Learning ◽

Sensitivity Analysis ◽

Uncertainty Quantification ◽

Cardiac Electrophysiology ◽

Reduced Order Modeling ◽

Reduced Order

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Global Sensitivity Analysis of a Homogenized Constrained Mixture Model of Arterial Growth and Remodeling

Journal of Elasticity ◽

10.1007/s10659-021-09833-9 ◽

2021 ◽

Author(s):

Sebastian Brandstaeter ◽

Sebastian L. Fuchs ◽

Jonas Biehler ◽

Roland C. Aydin ◽

Wolfgang A. Wall ◽

...

Keyword(s):

Sensitivity Analysis ◽

Global Sensitivity Analysis ◽

Model Parameters ◽

Computational Studies ◽

Growth And Remodeling ◽

Global Sensitivity ◽

Output Variability ◽

Constrained Mixture ◽

The One ◽

Influential Parameters

AbstractGrowth and remodeling in arterial tissue have attracted considerable attention over the last decade. Mathematical models have been proposed, and computational studies with these have helped to understand the role of the different model parameters. So far it remains, however, poorly understood how much of the model output variability can be attributed to the individual input parameters and their interactions. To clarify this, we propose herein a global sensitivity analysis, based on Sobol indices, for a homogenized constrained mixture model of aortic growth and remodeling. In two representative examples, we found that 54–80% of the long term output variability resulted from only three model parameters. In our study, the two most influential parameters were the one characterizing the ability of the tissue to increase collagen production under increased stress and the one characterizing the collagen half-life time. The third most influential parameter was the one characterizing the strain-stiffening of collagen under large deformation. Our results suggest that in future computational studies it may - at least in scenarios similar to the ones studied herein - suffice to use population average values for the other parameters. Moreover, our results suggest that developing methods to measure the said three most influential parameters may be an important step towards reliable patient-specific predictions of the enlargement of abdominal aortic aneurysms in clinical practice.

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Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Energies ◽

10.3390/en13174290 ◽

2020 ◽

Vol 13 (17) ◽

pp. 4290

Author(s):

Dongmei Zhang ◽

Yuyang Zhang ◽

Bohou Jiang ◽

Xinwei Jiang ◽

Zhijiang Kang

Keyword(s):

Sensitivity Analysis ◽

Gaussian Processes ◽

Latent Variable ◽

History Matching ◽

Latent Variable Models ◽

High Dimensional ◽

Model Parameters ◽

Variable Model ◽

Assisted History Matching ◽

Proxy Models

Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

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Cost-effectiveness of TAS-102 plus bevacizumab versus TAS-102 monotherapy in patients with metastatic colorectal cancer

BMC Gastroenterology ◽

10.1186/s12876-021-01771-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Kiyoaki Sugiura ◽

Yuki Seo ◽

Takayuki Takahashi ◽

Hideyuki Tokura ◽

Yasuhiro Ito ◽

...

Keyword(s):

Colorectal Cancer ◽

Health Care ◽

Sensitivity Analysis ◽

Cost Effectiveness ◽

Combination Therapy ◽

Metastatic Colorectal Cancer ◽

Cost Effective ◽

Model Parameters ◽

Combination Regimen ◽

The Cost

Abstract Background TAS-102 plus bevacizumab is an anticipated combination regimen for patients who have metastatic colorectal cancer. However, evidence supporting its use for this indication is limited. We compared the cost-effectiveness of TAS-102 plus bevacizumab combination therapy with TAS-102 monotherapy for patients with chemorefractory metastatic colorectal cancer. Method Markov decision modeling using treatment costs, disease-free survival, and overall survival was performed to examine the cost-effectiveness of TAS-102 plus bevacizumab combination therapy and TAS-102 monotherapy. The Japanese health care payer’s perspective was adopted. The outcomes were modeled on the basis of published literature. The incremental cost-effectiveness ratio (ICER) between the two treatment regimens was the primary outcome. Sensitivity analysis was performed and the effect of uncertainty on the model parameters were investigated. Results TAS-102 plus bevacizumab had an ICER of $21,534 per quality-adjusted life-year (QALY) gained compared with TAS-102 monotherapy. Sensitivity analysis demonstrated that TAS-102 monotherapy was more cost-effective than TAS-102 and bevacizumab combination therapy at a willingness-to-pay of under $50,000 per QALY gained. Conclusions TAS-102 and bevacizumab combination therapy is a cost-effective option for patients who have metastatic colorectal cancer in the Japanese health care system.

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Temporal stability of electrical conductivity in a sandy soil

International Agrophysics ◽

10.1515/intag-2016-0005 ◽

2016 ◽

Vol 30 (3) ◽

pp. 349-357 ◽

Cited By ~ 12

Author(s):

Aura Pedrera-Parrilla ◽

Eric C. Brevik ◽

Juan V. Giráldez ◽

Karl Vanderlinden

Keyword(s):

Electrical Conductivity ◽

Spatial Variability ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Precision Agriculture ◽

Temporal Stability ◽

Principal Component ◽

Model Parameters ◽

Soil Spatial Variability

Abstract Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

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