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A Bayesian approach to tracking patients having changing pharmacokinetic parametersThis 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.
Document ID
20050150740
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Bayard, David S.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA United States)
Jelliffe, Roger W.
Date Acquired
August 23, 2013
Publication Date
February 1, 2004
Publication Information
Publication: Journal of pharmacokinetics and pharmacodynamics
Volume: 31
Issue: 1
ISSN: 1567-567X
Subject Category
Life Sciences (General)
Distribution Limits
Public
Copyright
Other

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