SAXTON, Harry, XU, Xu, HALLIDAY, Ian and SCHENKEL, Torsten (2023). New perspectives on sensitivity and identifiability analysis using the unscented kalman filter. [Pre-print] (Unpublished)
Preprints have not been peer-reviewed. They should not be relied on to guide clinical practice or health related behaviour and should not be regarded as conclusive or be reported in news media as established information.
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2306.15710v1.pdf - Pre-print
Available under License Creative Commons Attribution.
2306.15710v1.pdf - Pre-print
Available under License Creative Commons Attribution.
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Abstract
Detailed dynamical systems' models used in the life sciences may include
hundreds of state variables and many input parameters, often with physical
meaning. Therefore, efficient and unique input parameter identification, from
experimental data, is an essential but challenging task for this class of
model. To clarify our understating of the process (which within a clinical
context amounts to a personalisation), we utilise the computational methods of
Unscented Kalman filtration (UKF), sensitivity and orthogonality analysis. We
have applied these three techniques to a test-bench model of a single
ventricle, coupled, via Ohmic valves, to a Compliance-Resistor-Compliance (CRC)
Windkessel electrical analogue model of the systemic circulation, chosen in
view of its relative simplicity, interpretability and prior art. Utilising an
efficient, novel and real-time implementation of the UKF (Code available at
https://github.com/H-Sax/CMSB-2023), we show how, counter-intuitively, input
parameters are efficiently recovered from experimental data \emph{even if they
are not sensitive parameters in the currently accepted sense}. This result (i)
exposes potential limitations in the standard interpretation of what it means
for an input parameter to be designated identifiable and (ii) suggests that the
concepts of sensitivity and identifiability may have a weaker relationship than
commonly thought - at least in the presence of an appropriate data set. We
rationalise these observations.
Practically, we present results which show the UKF to be an efficient method
for assigning personalised input parameters from experimental data in
real-time, which enhances the clinical significance of our approach.
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