Assessing input parameter hyperspace and parameter identifiability in a cardiovascular system model via sensitivity analysis

SAXTON, Harry, XU, Xu, SCHENKEL, Torsten and HALLIDAY, Ian (2024). Assessing input parameter hyperspace and parameter identifiability in a cardiovascular system model via sensitivity analysis. Journal of Computational Science, 79: 102287.

Preview

PDF Saxton-AssessingInputParameter(VoR).pdf - Published Version Creative Commons Attribution. Download (1MB) | Preview

Abstract

We aim to clarify our understanding of the process of state-space model input parameter identification, known, within the clinical context, as model personalisation. To do so, we apply reference sensitivity and identifiability techniques to a lumped parameter, single ventricle representation of the systemic circulation, chosen in view of its relative simplicity and prior art. We attempt to quantify the reliability of input parameter identifiability through the lens of 4 clinically relevant measurements and the attendant difficulty in personalising the model. In turn, this that we extend existing methods which combine both parameter influence and orthogonality, to global sensitivities. By examining different parameter sensitivity evaluation methodologies, we investigate the stability of optimal parameter subsets which are commonly used to aid clinical investigations. In order to perform the personalisation process, one must understand the complexity of the high dimensional input parameter hyperspace associated with this class of model. By utilising Sobol indices, we propose a domain-agnostic and intuitive approach. This involves varying the bounds of the input parameter space relative to the model’s base state. These investigations yield a pseudo-mapping of the input hyperspace, cementing our understanding of the role of identifiable input parameters in the state-space model. Our findings suggest a novel global methodology for input parameter identifiability and input hyperspace mapping, providing valuable insights into solving the personalisation process.

Item Type:	Article
Additional Information:	** Article version: VoR ** From Elsevier via Jisc Publications Router ** Licence for VoR version of this article starting on 12-04-2024: http://creativecommons.org/licenses/by/4.0/ **Journal IDs: issn 18777503 **History: issued 31-07-2024; published_online 20-04-2024; accepted 03-04-2024
Identification Number:	https://doi.org/10.1016/j.jocs.2024.102287
SWORD Depositor:	Colin Knott
Depositing User:	Colin Knott
Date Deposited:	23 Apr 2024 15:55
Last Modified:	23 Apr 2024 16:00
URI:	https://shura.shu.ac.uk/id/eprint/33615

Actions (login required)

View Item

Downloads

Publication Metrics