[2403.16742] A Branch and Bound method for the exact parameter identification of the PK/PD model for anesthetic drugs
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Electrical Engineering and Systems Science > Systems and Control

arXiv:2403.16742 (eess)
[Submitted on 25 Mar 2024]

Title:A Branch and Bound method for the exact parameter identification of the PK/PD model for anesthetic drugs

Authors:Giulia Di Credico, Luca Consolini, Mattia Laurini, Marco Locatelli, Marco Milanesi, Michele Schiavo, Antonio Visioli
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Abstract:We address the problem of parameter identification for the standard pharmacokinetic/pharmacodynamic (PK/PD) model for anesthetic drugs. Our main contribution is the development of a global optimization method that guarantees finding the parameters that minimize the one-step ahead prediction error. The method is based on a branch-and-bound algorithm, that can be applied to solve a more general class of nonlinear regression problems. We present some simulation results, based on a dataset of twelve patients. In these simulations, we are always able to identify the exact parameters, despite the non-convexity of the overall identification problem.
Subjects: Systems and Control (eess.SY)
Cite as: arXiv:2403.16742 [eess.SY]
  (or arXiv:2403.16742v1 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2403.16742
Journal reference: 2024 IEEE 63rd Conference on Decision and Control (CDC), Milan, Italy, 2024, 8754-8759
Related DOI: https://doi.org/10.1109/CDC56724.2024.10885926

Submission history

From: Giulia Di Credico Dr [view email]
[v1] Mon, 25 Mar 2024 13:13:39 UTC (339 KB)
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