ArticleComputers in biology and medicine2023
UncertainSCI: Uncertainty quantification for computational models in biomedicine and bioengineering.
Article in Computers in biology and medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Integrating Uncertainty Quantification into Computational Fluid Dynamics Models of Coronary Arteries Under Steady Flow.Journal of biomechanical engineering · 2026Article
- Uncertainty should be the rule, not the exception: Integrating fibre orientation variability in cardiac electrophysiology modelling.The Journal of physiology · 2026Article
- Uncertainty quantification via polynomial chaos expansion of myocardial fibre orientation and cardiac activation patterns.The Journal of physiology · 2026Article
- Uncertainty quantification of conduction velocity in models of cardiac spread of activation.Medical & biological engineering & computing · 2026Article
- Uncertainty Quantification forChemical reviews · 2026Review
- Advanced FE simulation coupled with statistical surrogate modeling toward a multifactorial view on the pelvic floor muscle damage and perineal tearing during childbirth.Medical & biological engineering & computing · 2026Article
- Roadmap towards personalized approaches and safety considerations in non-ionizing radiation: from dosimetry to therapeutic and diagnostic applications.Physics in medicine and biology · 2026Review
- Molecular dynamics simulations of proteins: an in-depth review of computational strategies, structural insights, and their role in medicinal chemistry and drug development.Biological cybernetics · 2025Review
- A computational study on the influence of antegrade accessory pathway location on the 12-lead electrocardiogram in Wolff-Parkinson-White syndrome.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Self-learning salp swarm algorithm for global optimization and its application in multi-layer perceptron model training.Scientific reports · 2024Article
- Global and local identifiability analysis of a nonlinear biphasic constitutive model in confined compression.Journal of the Royal Society, Interface · 2024Article
- Amputated life-testing based on extended Dagum percentiles for type of group inspection plans: optimal sample sizes, termination time ratios analysis.Scientific reports · 2024Article
- Application of Order and Sample Selection in Uncertainty Quantification of Cardiac Models.Computing in cardiology · 2024Article
- Uncertainty Quantification of Fibrotic Conductivity Effects on Computational Model-Derived Ablation of Atypical Left Atrial Flutter.Computing in cardiology · 2024Article
- On the uncertainty quantification of the active uterine contraction during the second stage of labor simulation.Medical & biological engineering & computing · 2024Article
- Article
- Uncertainty Quantification of Fiber Orientation and Epicardial Activation.Computing in cardiology · 2023Article
- A Grid Search of Fibrosis Thresholds for Uncertainty Quantification in Atrial Flutter Simulations.Computing in cardiology · 2023Article
- Uncertainty Quantification of the Effect of Variable Conductivity in Ventricular Fibrotic Regions on Ventricular Tachycardia.Computing in cardiology · 2023Article
- Capturing the Influence of Conduction Velocity on Epicardial Activation Patterns Using Uncertainty Quantification.Computing in cardiology · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundComputational biomedical simulations frequently contain parameters that model physical features, material coefficients, and physiological effects, whose values are typically assumed known a priori. Understanding the effect of variability in those assumed values is currently a topic of great interest. A general-purpose software tool that quantifies how variation in these parameters affects model outputs is not broadly available in biomedicine. For this reason, we developed the 'UncertainSCI' uncertainty quantification software suite to facilitate analysis of uncertainty due to parametric variability.
methodsWe developed and distributed a new open-source Python-based software tool, UncertainSCI, which employs advanced parameter sampling techniques to build polynomial chaos (PC) emulators that can be used to predict model outputs for general parameter values. Uncertainty of model outputs is studied by modeling parameters as random variables, and model output statistics and sensitivities are then easily computed from the emulator. Our approaches utilize modern, near-optimal techniques for sampling and PC construction based on weighted Fekete points constructed by subsampling from a suitably randomized candidate set.
resultsConcentrating on two test cases-modeling bioelectric potentials in the heart and electric stimulation in the brain-we illustrate the use of UncertainSCI to estimate variability, statistics, and sensitivities associated with multiple parameters in these models.
conclusionUncertainSCI is a powerful yet lightweight tool enabling sophisticated probing of parametric variability and uncertainty in biomedical simulations. Its non-intrusive pipeline allows users to leverage existing software libraries and suites to accurately ascertain parametric uncertainty in a variety of applications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.