UncertainSCI: Uncertainty quantification for computational models in biomedicine and bioengineering. (January 2023)

• Record Type: Journal Article
• Authors: Narayan, Akil; Liu, Zexin; Bergquist, Jake A.; Charlebois, Chantel; Rampersad, Sumientra; Rupp, Lindsay; Brooks, Dana; White, Dan; Tate, Jess; MacLeod, Rob S.
• Is Part Of: Computers in biology and medicine. Volume 152(2023)
• Publication Date: 2023-01
• Subjects: Biomedical simulations -- Uncertainty quantification -- Open-source software; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106407
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Computational 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. Methods: We 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. Results: Concentrating 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. Conclusion: UncertainSCI 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. Highlights: UncertainSCI is novel software that implements support for uncertainty quantification UncertainSCI is open-source Python software connecting to other simulation packages. With four biomedical examples, we demonstrate usability and accuracy of UncertainSCI.