{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:57:15Z","timestamp":1782986235154,"version":"3.54.5"},"reference-count":74,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010663","name":"European Research Council","doi-asserted-by":"publisher","award":["864313"],"award-info":[{"award-number":["864313"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1016\/j.compbiomed.2025.110381","type":"journal-article","created":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T06:40:32Z","timestamp":1748673632000},"page":"110381","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"special_numbering":"C","title":["A comparative analysis of metamodels for 0D cardiovascular models, and pipeline for sensitivity analysis, parameter estimation, and uncertainty quantification"],"prefix":"10.1016","volume":"193","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-5120-2134","authenticated-orcid":false,"given":"John M.","family":"Hanna","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0004-5714-6307","authenticated-orcid":false,"given":"Pavlos","family":"Varsos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00e9r\u00f4me","family":"Kowalski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-8878-0616","authenticated-orcid":false,"given":"Lorenzo","family":"Sala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-6688-0915","authenticated-orcid":false,"given":"Roel","family":"Meiburg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-9754-4386","authenticated-orcid":false,"given":"Irene E.","family":"Vignon-Clementel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compbiomed.2025.110381_b1","series-title":"The finite element method: its basis and fundamentals","author":"Zienkiewicz","year":"2005"},{"key":"10.1016\/j.compbiomed.2025.110381_b2","series-title":"The finite volume method","author":"Moukalled","year":"2016"},{"issue":"A93- 25826 09- 90","key":"10.1016\/j.compbiomed.2025.110381_b3","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1146\/annurev.aa.30.090192.002551","article-title":"Smoothed particle hydrodynamics","volume":"30","author":"Monaghan","year":"1992","journal-title":"Annu. Rev. Astron. Astrophys."},{"key":"10.1016\/j.compbiomed.2025.110381_b4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1475-925X-10-33","article-title":"Review of zero-D and 1-D models of blood flow in the cardiovascular system","volume":"10","author":"Shi","year":"2011","journal-title":"Biomed. Eng. Online"},{"key":"10.1016\/j.compbiomed.2025.110381_b5","series-title":"Quantification of Biophysical Parameters in Medical Imaging","first-page":"45","article-title":"Mathematical modeling of blood flow in the cardiovascular system","author":"Caiazzo","year":"2018"},{"issue":"2","key":"10.1016\/j.compbiomed.2025.110381_b6","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s10237-021-01545-2","article-title":"Multiscale modelling of potts shunt as a potential palliative treatment for suprasystemic idiopathic pulmonary artery hypertension: a paediatric case study","volume":"21","author":"Pant","year":"2022","journal-title":"Biomech. Model. Mechanobiol."},{"issue":"5","key":"10.1016\/j.compbiomed.2025.110381_b7","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Netw."},{"key":"10.1016\/j.compbiomed.2025.110381_b8","doi-asserted-by":"crossref","DOI":"10.1155\/2018\/7068349","article-title":"Deep learning for computer vision: A brief review","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.compbiomed.2025.110381_b9","first-page":"603","article-title":"Natural language processing","author":"Chowdhary","year":"2020","journal-title":"Fundam. Artif. Intell."},{"key":"10.1016\/j.compbiomed.2025.110381_b10","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.3389\/fphys.2020.01086","article-title":"Implementation and calibration of a deep neural network to predict parameters of left ventricular systolic function based on pulmonary and systemic arterial pressure signals","volume":"11","author":"Bonnemain","year":"2020","journal-title":"Front. Physiol."},{"key":"10.1016\/j.compbiomed.2025.110381_b11","doi-asserted-by":"crossref","DOI":"10.3389\/fcvm.2021.752088","article-title":"Deep neural network to accurately predict left ventricular systolic function under mechanical assistance","volume":"8","author":"Bonnemain","year":"2021","journal-title":"Front. Cardiovasc. Med."},{"key":"10.1016\/j.compbiomed.2025.110381_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2019.112623","article-title":"Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks","volume":"358","author":"Kissas","year":"2020","journal-title":"Comput. Methods Appl. Mech. Engrg."},{"key":"10.1016\/j.compbiomed.2025.110381_b13","series-title":"Medical Imaging with Deep Learning","article-title":"Finite volume informed graph neural network for myocardial perfusion simulation","author":"de Chou","year":"2024"},{"key":"10.1016\/j.compbiomed.2025.110381_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107676","article-title":"Learning reduced-order models for cardiovascular simulations with graph neural networks","volume":"168","author":"Pegolotti","year":"2024","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.110381_b15","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1038\/s41746-024-01084-x","article-title":"Whole-heart electromechanical simulations using latent neural ordinary differential equations","volume":"7","author":"Salvador","year":"2024","journal-title":"NPJ Digit. Med."},{"issue":"4","key":"10.1016\/j.compbiomed.2025.110381_b16","doi-asserted-by":"crossref","first-page":"897","DOI":"10.2307\/2371268","article-title":"The homogeneous chaos","volume":"60","author":"Wiener","year":"1938","journal-title":"Amer. J. Math."},{"issue":"7","key":"10.1016\/j.compbiomed.2025.110381_b17","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1016\/j.ress.2007.04.002","article-title":"Global sensitivity analysis using polynomial chaos expansions","volume":"93","author":"Sudret","year":"2008","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.110381_b18","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1007\/s10439-022-03098-6","article-title":"Sensitivity analysis of a mathematical model simulating the post-hepatectomy hemodynamics response","volume":"51","author":"Sala","year":"2023","journal-title":"Ann. Biomed. Eng."},{"issue":"8","key":"10.1016\/j.compbiomed.2025.110381_b19","doi-asserted-by":"crossref","DOI":"10.1002\/cnm.2755","article-title":"A guide to uncertainty quantification and sensitivity analysis for cardiovascular applications","volume":"32","author":"Eck","year":"2016","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"issue":"10","key":"10.1016\/j.compbiomed.2025.110381_b20","doi-asserted-by":"crossref","DOI":"10.1002\/cnm.3388","article-title":"Uncertainty in model-based treatment decision support: Applied to aortic valve stenosis","volume":"36","author":"Meiburg","year":"2020","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"key":"10.1016\/j.compbiomed.2025.110381_b21","doi-asserted-by":"crossref","DOI":"10.1002\/cnm.3836","article-title":"Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for vascular haemodynamics","author":"Sch\u00e4fer","year":"2024","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.110381_b22","doi-asserted-by":"crossref","DOI":"10.1115\/1.4044502","article-title":"A metamodeling approach for instant severity assessment and uncertainty quantification of iliac artery stenoses","volume":"142","author":"Heinen","year":"2020","journal-title":"J. Biomech. Eng."},{"issue":"02","key":"10.1016\/j.compbiomed.2025.110381_b23","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1142\/S0129065704001899","article-title":"Gaussian processes for machine learning","volume":"14","author":"Seeger","year":"2004","journal-title":"Int. J. Neural Syst."},{"issue":"2","key":"10.1016\/j.compbiomed.2025.110381_b24","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TPAMI.2013.218","article-title":"Gaussian processes for data-efficient learning in robotics and control","volume":"37","author":"Deisenroth","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"10.1016\/j.compbiomed.2025.110381_b25","article-title":"Probabilistic non-linear principal component analysis with Gaussian process latent variable models.","volume":"6","author":"Lawrence","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.compbiomed.2025.110381_b26","doi-asserted-by":"crossref","first-page":"364","DOI":"10.3389\/fphys.2020.00364","article-title":"Sensitivity and uncertainty analysis of two human atrial cardiac cell models using Gaussian process emulators","volume":"11","author":"Coveney","year":"2020","journal-title":"Front. Physiol."},{"key":"10.1016\/j.compbiomed.2025.110381_b27","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.3389\/fphys.2018.01002","article-title":"Gaussian process regressions for inverse problems and parameter searches in models of ventricular mechanics","volume":"9","author":"Di Achille","year":"2018","journal-title":"Front. Physiol."},{"issue":"6","key":"10.1016\/j.compbiomed.2025.110381_b28","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1011257","article-title":"Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators","volume":"19","author":"Strocchi","year":"2023","journal-title":"PLoS Comput. Biol."},{"issue":"2","key":"10.1016\/j.compbiomed.2025.110381_b29","doi-asserted-by":"crossref","DOI":"10.1002\/cnm.3421","article-title":"Markov chain Monte Carlo with Gaussian processes for fast parameter estimation and uncertainty quantification in a 1D fluid-dynamics model of the pulmonary circulation","volume":"37","author":"Paun","year":"2021","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"issue":"217","key":"10.1016\/j.compbiomed.2025.110381_b30","doi-asserted-by":"crossref","DOI":"10.1098\/rsif.2024.0194","article-title":"Reconstructing blood flow in data-poor regimes: a vasculature network kernel for Gaussian process regression","volume":"21","author":"Ashtiani","year":"2024","journal-title":"J. R. Soc. Interface"},{"key":"10.1016\/j.compbiomed.2025.110381_b31","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.jbiomech.2016.11.037","article-title":"Partial hepatectomy hemodynamics changes: Experimental data explained by closed-loop lumped modeling","volume":"50","author":"Audebert","year":"2017","journal-title":"J. Biomech."},{"issue":"3","key":"10.1016\/j.compbiomed.2025.110381_b32","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/j.jhep.2020.10.036","article-title":"Predicting the risk of post-hepatectomy portal hypertension using a digital twin: A clinical proof of concept","volume":"74","author":"Golse","year":"2021","journal-title":"J. Hepatol."},{"key":"10.1016\/j.compbiomed.2025.110381_b33","series-title":"Cardiovascular Mathematics: Modeling and simulation of the circulatory system","author":"Formaggia","year":"2010"},{"key":"10.1016\/j.compbiomed.2025.110381_b34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1475-925X-10-33","article-title":"Review of zero-D and 1-D models of blood flow in the cardiovascular system","volume":"10","author":"Shi","year":"2011","journal-title":"Biomed. Eng. Online"},{"key":"10.1016\/j.compbiomed.2025.110381_b35","doi-asserted-by":"crossref","DOI":"10.1113\/JP287929","article-title":"A comparative study of lumped heart models for personalized medicine through sensitivity and identifiability analysis","author":"Haghebaert","year":"2025","journal-title":"The Journal of Physiology"},{"issue":"5","key":"10.1016\/j.compbiomed.2025.110381_b36","doi-asserted-by":"crossref","first-page":"H2076","DOI":"10.1152\/ajpheart.2001.280.5.H2076","article-title":"Modeling of the norwood circulation: effects of shunt size, vascular resistances, and heart rate","volume":"280","author":"Migliavacca","year":"2001","journal-title":"Am. J. Physiology- Hear. Circ. Physiol."},{"issue":"6\u20137","key":"10.1016\/j.compbiomed.2025.110381_b37","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1002\/cnm.1466","article-title":"A simple, versatile valve model for use in lumped parameter and one-dimensional cardiovascular models","volume":"28","author":"Mynard","year":"2012","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"issue":"11","key":"10.1016\/j.compbiomed.2025.110381_b38","doi-asserted-by":"crossref","first-page":"2450","DOI":"10.1109\/TBME.2018.2797999","article-title":"A lumped parameter model to study atrioventricular valve regurgitation in stage 1 and changes across stage 2 surgery in single ventricle patients","volume":"65","author":"Pant","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"11","key":"10.1016\/j.compbiomed.2025.110381_b39","doi-asserted-by":"crossref","first-page":"2162","DOI":"10.1016\/j.jbiomech.2015.11.030","article-title":"Data assimilation and modelling of patient-specific single-ventricle physiology with and without valve regurgitation","volume":"49","author":"Pant","year":"2016","journal-title":"J. Biomech."},{"issue":"4","key":"10.1016\/j.compbiomed.2025.110381_b40","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1016\/j.jhep.2011.12.001","article-title":"EASL-EORTC clinical practice guidelines: management of hepatocellular carcinoma","volume":"56","author":"Liver","year":"2012","journal-title":"J. Hepatol."},{"issue":"5","key":"10.1016\/j.compbiomed.2025.110381_b41","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1097\/SLA.0b013e3182a64b38","article-title":"Posthepatectomy portal vein pressure predicts liver failure and mortality after major liver resection on noncirrhotic liver","volume":"258","author":"Allard","year":"2013","journal-title":"Ann. Surg."},{"issue":"16","key":"10.1016\/j.compbiomed.2025.110381_b42","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1056\/NEJMra035488","article-title":"Pulmonary arterial hypertension","volume":"351","author":"Farber","year":"2004","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.compbiomed.2025.110381_b43","article-title":"Thirty years of surgical management of pediatric pulmonary hypertension: Mid-term outcomes following reverse potts shunt and transplantation","author":"Valdeolmillos","year":"2023","journal-title":"J. Thorac. Cardiovasc. Surg."},{"issue":"6","key":"10.1016\/j.compbiomed.2025.110381_b44","doi-asserted-by":"crossref","DOI":"10.1056\/NEJM200402053500623","article-title":"Potts shunt in patients with pulmonary hypertension","volume":"350","author":"Blanc","year":"2004","journal-title":"N. Engl. J. Med."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.110381_b45","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/S0006-3495(91)82201-9","article-title":"Relation between left ventricular cavity pressure and volume and systolic fiber stress and strain in the wall","volume":"59","author":"Arts","year":"1991","journal-title":"Biophys. J."},{"key":"10.1016\/j.compbiomed.2025.110381_b46","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.1007\/s10439-006-9189-2","article-title":"Dependence of intramyocardial pressure and coronary flow on ventricular loading and contractility: a model study","volume":"34","author":"Bovendeerd","year":"2006","journal-title":"Ann. Biomed. Eng."},{"issue":"3","key":"10.1016\/j.compbiomed.2025.110381_b47","doi-asserted-by":"crossref","DOI":"10.1115\/1.4029909","article-title":"Multiscale modeling of cardiovascular flows for clinical decision support","volume":"67","author":"Marsden","year":"2015","journal-title":"Appl. Mech. Rev."},{"issue":"5","key":"10.1016\/j.compbiomed.2025.110381_b48","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1016\/j.jbiomech.2004.05.027","article-title":"Multiscale modeling of the cardiovascular system: application to the study of pulmonary and coronary perfusions in the univentricular circulation","volume":"38","author":"Lagana","year":"2005","journal-title":"J. Biomech."},{"issue":"6","key":"10.1016\/j.compbiomed.2025.110381_b49","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1016\/j.jbiomech.2005.02.021","article-title":"Multiscale modelling in biofluidynamics: application to reconstructive paediatric cardiac surgery","volume":"39","author":"Migliavacca","year":"2006","journal-title":"J. Biomech."},{"key":"10.1016\/j.compbiomed.2025.110381_b50","series-title":"Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: 4th International Workshop, STACOM 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 26, 2013. Revised Selected Papers 4","first-page":"102","article-title":"A multiscale filtering-based parameter estimation method for patient-specific coarctation simulations in rest and exercise","author":"Pant","year":"2014"},{"issue":"12","key":"10.1016\/j.compbiomed.2025.110381_b51","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.1002\/cnm.2692","article-title":"A methodological paradigm for patient-specific multi-scale cfd simulations: from clinical measurements to parameter estimates for individual analysis","volume":"30","author":"Pant","year":"2014","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"issue":"64\u201367","key":"10.1016\/j.compbiomed.2025.110381_b52","first-page":"2","article-title":"Recurrent neural networks","volume":"5","author":"Medsker","year":"2001","journal-title":"Des. Appl."},{"key":"10.1016\/j.compbiomed.2025.110381_b53","doi-asserted-by":"crossref","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","author":"Hochreiter","year":"1997","journal-title":"Neural Comput. MIT- Press"},{"key":"10.1016\/j.compbiomed.2025.110381_b54","series-title":"TensorFlow: Large-scale machine learning on heterogeneous systems","author":"Abadi","year":"2015"},{"issue":"6","key":"10.1016\/j.compbiomed.2025.110381_b55","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1016\/j.crme.2008.02.013","article-title":"Sparse polynomial chaos expansions and adaptive stochastic finite elements using a regression approach","volume":"336","author":"Blatman","year":"2008","journal-title":"C. R. M\u00e9canique"},{"issue":"6","key":"10.1016\/j.compbiomed.2025.110381_b56","doi-asserted-by":"crossref","first-page":"2345","DOI":"10.1016\/j.jcp.2010.12.021","article-title":"Adaptive sparse polynomial chaos expansion based on least angle regression","volume":"230","author":"Blatman","year":"2011","journal-title":"J. Comput. Phys."},{"key":"10.1016\/j.compbiomed.2025.110381_b57","series-title":"Open TURNS: An industrial software for uncertainty quantification in simulation","author":"Baudin","year":"2015"},{"key":"10.1016\/j.compbiomed.2025.110381_b58","article-title":"Probabilistic surrogate modeling by Gaussian process: A review on recent insights in estimation and validation","author":"Marrel","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.compbiomed.2025.110381_b59","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.compbiomed.2025.110381_b60","series-title":"Sensitivity analysis in practice: a guide to assessing scientific models","author":"Saltelli","year":"2004"},{"issue":"1\u20133","key":"10.1016\/j.compbiomed.2025.110381_b61","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/S0378-4754(00)00270-6","article-title":"Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates","volume":"55","author":"Sobol","year":"2001","journal-title":"Math. Comput. Simulation"},{"issue":"9","key":"10.1016\/j.compbiomed.2025.110381_b62","doi-asserted-by":"crossref","first-page":"97","DOI":"10.21105\/joss.00097","article-title":"SALib: An open-source python library for sensitivity analysis","volume":"2","author":"Herman","year":"2017","journal-title":"J. Open Source Softw."},{"issue":"2","key":"10.1016\/j.compbiomed.2025.110381_b63","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1080\/00401706.1991.10484804","article-title":"Factorial sampling plans for preliminary computational experiments","volume":"33","author":"Morris","year":"1991","journal-title":"Technometrics"},{"key":"10.1016\/j.compbiomed.2025.110381_b64","series-title":"TruWave pressure monitoring kit with TruWave disposable pressure transducer","year":"2024"},{"key":"10.1016\/j.compbiomed.2025.110381_b65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13054-017-1737-7","article-title":"Transthoracic echocardiography: an accurate and precise method for estimating cardiac output in the critically ill patient","volume":"21","author":"Mercado","year":"2017","journal-title":"Crit. Care"},{"issue":"3","key":"10.1016\/j.compbiomed.2025.110381_b66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pcbi.1006828","article-title":"Scalable nonlinear programming framework for parameter estimation in dynamic biological system models","volume":"15","author":"Shin","year":"2019","journal-title":"PLoS Comput. Biol."},{"issue":"3","key":"10.1016\/j.compbiomed.2025.110381_b67","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1109\/TCST.2004.824799","article-title":"Selection of model parameters for off-line parameter estimation","volume":"12","author":"Li","year":"2004","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"10.1016\/j.compbiomed.2025.110381_b68","doi-asserted-by":"crossref","DOI":"10.1016\/j.jocs.2023.102158","article-title":"Personalised parameter estimation of the cardiovascular system: Leveraging data assimilation and sensitivity analysis","volume":"74","author":"Saxton","year":"2023","journal-title":"J. Comput. Sci."},{"key":"10.1016\/j.compbiomed.2025.110381_b69","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2024.116846","article-title":"InVAErt networks: A data-driven framework for model synthesis and identifiability analysis","volume":"423","author":"Tong","year":"2024","journal-title":"Comput. Methods Appl. Mech. Engrg."},{"issue":"9","key":"10.1016\/j.compbiomed.2025.110381_b70","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0162366","article-title":"Driving the model to its limit: profile likelihood based model reduction","volume":"11","author":"Maiwald","year":"2016","journal-title":"PloS One"},{"issue":"8","key":"10.1016\/j.compbiomed.2025.110381_b71","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1093\/bioinformatics\/bts088","article-title":"An integrated strategy for prediction uncertainty analysis","volume":"28","author":"Vanlier","year":"2012","journal-title":"Bioinformatics"},{"issue":"4","key":"10.1016\/j.compbiomed.2025.110381_b72","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1107\/S2053273315007123","article-title":"Diaphony, a measure of uniform distribution, and the patterson function","volume":"71","author":"Hornfeck","year":"2015","journal-title":"Acta Crystallogr. Sect. A: Found. Adv."},{"issue":"2173","key":"10.1016\/j.compbiomed.2025.110381_b73","doi-asserted-by":"crossref","DOI":"10.1098\/rsta.2019.0347","article-title":"Parameter subset reduction for patient-specific modelling of arrhythmogenic cardiomyopathy-related mutation carriers in the CircAdapt model","volume":"378","author":"Van Osta","year":"2020","journal-title":"Phil. Trans. R. Soc. A"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.110381_b74","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1186\/s12938-024-01232-0","article-title":"Parameter subset reduction for imaging-based digital twin generation of patients with left ventricular mechanical discoordination","volume":"23","author":"Koopsen","year":"2024","journal-title":"BioMed. Eng. OnLine"}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0010482525007322?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0010482525007322?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T17:35:17Z","timestamp":1766424917000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482525007322"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":74,"alternative-id":["S0010482525007322"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.compbiomed.2025.110381","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A comparative analysis of metamodels for 0D cardiovascular models, and pipeline for sensitivity analysis, parameter estimation, and uncertainty quantification","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.compbiomed.2025.110381","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110381"}}