Quantifying uncertainty bounds in anesthetic PKPD models.

  • Bibian S
  • Dumont G
  • Huzmezan M
 et al. 
  • 4

    Readers

    Mendeley users who have this article in their library.
  • 3

    Citations

    Citations of this article.

Abstract

A major challenge faced when designing controllers to automate anesthetic drug delivery is the large variability that exists between and within patients. This intra- and inter-patient variability have been reported to lead to instability. Hence, defining and quantifying uncertainty bounds provides a mean to validate the control design, ensure its stability and assess performance. In this work, the intra- and inter-patient variability measured from thiopental induction data is used to define uncertainty bounds. It is shown that these bounds can be reduced by up to 40% when using a patient-specific model as compared to a population-normed model. It is also shown that identifying only the overall static gain of the patient system already decreases significantly this uncertainty.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Stéphane Bibian

  • Guy a Dumont

  • Mihai Huzmezan

  • Craig R Ries

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free