RESEARCH ARTICLE


The Impact of Parameter Identification Methods on Drug Therapy Control in an Intensive Care Unit



Christopher E Hann*, 1, J. Geoffrey Chase1, Michael F Ypma2, Jos Elfring2, NoorHafiz Mohd Noraff1, Piers Lawrence1, Geoffrey M Shaw3
1 Centre of Bio-Engineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
2 Control Systems Technology Group, Department of Mechanical Engineering, Eindhoven University of Technology, New Zealand
3 Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand


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© Hann et al.; Licensee Bentham Open.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.5/), which permits unrestrictive use, distribution, and reproduction in any medium, provided the original work is properly cited.

* Address correspondence to this author at the Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand; E-mail: Chris.Hann@canterbury.ac.nz


Abstract

This paper investigates the impact of fast parameter identification methods, which do not require any forward simulations, on model-based glucose control, using retrospective data in the Christchurch Hospital Intensive Care Unit. The integral-based identification method has been previously clinically validated and extensively applied in a number of biomedical applications; and is a crucial element in the presented model-based therapeutics approach. Common non-linear regression and gradient descent approaches are too computationally intense and not suitable for the glucose control applications presented. The main focus in this paper is on better characterizing and understanding the importance of the integral in the formulation and the effect it has on model-based drug therapy control. As a comparison, a potentially more natural derivative formulation which has the same computation speed advantages is investigated, and is shown to go unstable with respect to modelling error which is always present clinically. The integral method remains robust.