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Pharmacokinetic Parameter Estimation with Stochastic Dynamic Models

  • David Z. D’Argenio
  • Ruomei Zhang

Abstract

The pharmacokinetic parameter estimation problem is reexamined within the framework of stochastic dynamic systems. Using this formalism, two sources of uncertainty are incorporated into the parameter estimation procedure: measurement error and process or model error. Consideration is given to linear dynamic models, with both model and measurement error terms modeled as Gaussian random processes. The maximum likelihood estimate of the parameters is obtained by using the Kaiman filter formulation of the model to compute the likelihood function which is then maximized by direct nonlinear optimization. This approach to maximum likelihood estimation, given process or model error as well as output error, is evaluated using several simulated pharmacokinetic parameter estimation problems.

Keywords

Kalman Filter Extended Kalman Filter Process Noise Process Error Output Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • David Z. D’Argenio
    • 1
  • Ruomei Zhang
    • 1
  1. 1.Department of Biomedical EngineeringUniversity of Southern CaliforniaUSA

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