title: Quantifying uncertainty bounds in anesthetic PKPD models.
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.
authors.affiliation: Dept. of Electr. & Comput. Eng., British Columbia Univ., Vancouver, BC, Canada.
authors.forename: Stéphane
authors.initials: S
authors.lastname: Bibian
authors.forename: Guy A
authors.initials: GA
authors.lastname: Dumont
authors.forename: Mihai
authors.initials: M
authors.lastname: Huzmezan
authors.forename: Craig R
authors.initials: CR
authors.lastname: Ries