A Branch and Bound method for the exact parameter identification of the PK/PD model for anesthetic drugs
arxiv(2024)
摘要
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.
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