A Bayesian approach to tracking patients having changing pharmacokinetic parameters.
This paper considers the updating of Bayesian posterior densities for pharmacokinetic models associated with patients having changing parameter values. For estimation purposes it is proposed to use the Interacting Multiple Model (IMM) estimation algorithm, which is currently a popular algorithm in the aerospace community for tracking maneuvering targets. The IMM algorithm is described, and compared to the multiple model (MM) and Maximum A-Posteriori (MAP) Bayesian estimation methods, which are presently used for posterior updating when pharmacokinetic parameters do not change. Both the MM and MAP Bayesian estimation methods are used in their sequential forms, to facilitate tracking of changing parameters. Results indicate that the IMM algorithm is well suited for tracking time-varying pharmacokinetic parameters in acutely ill and unstable patients, incurring only about half of the integrated error compared to the sequential MM and MAP methods on the same example.
Bayard
David S
DS
Senior Research Scientist, Mail Stop 198-326, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA. david.bayard@jpl.nasa.gov
Jelliffe
Roger W
RW
D000465:Algorithms; D001499:Bayes Theorem; D002986:Clinical Trials as Topic; D006801:Humans; D016014:Linear Models; D016011:Normal Distribution; D010599:Pharmacokinetics; D013997:Time Factors; D014031:Tobramycin / Q000493:pharmacokinetics
D014031:Tobramycin