DocumentCode :
1741397
Title :
Analysis of information content of pharmacokinetic data using generalized sensitivity functions
Author :
Thomaseth, Karl ; Cobelli, Claudio
Author_Institution :
LADSEB, CNR, Padova, Italy
Volume :
1
fYear :
2000
fDate :
2000
Firstpage :
435
Abstract :
Generalized Sensitivity Functions (GSF) have been recently introduced to overcome the limitation of traditional sensitivity analysis to reveal the information content about estimated model parameters of observations collected during an experiment. This aspect is relevant in mathematical modelling of physiological systems because understanding how the estimation of individual model parameters is related to observed system output patterns gives indications about the effective role of parameters in describing different physiological processes. GSF functions have been initially proposed with reference to continuous-time or frequently sampled data. In this study the common situation encountered in pharmacokinetic (PK) and metabolic studies of discrete-time, sparse data is considered. In this case GSF become staircase functions with step changes at each new data point, and with zero initial condition and final unit value after all data have been collected. The most informative data for a particular parameter are those that produce a major variation in the transition from 0 to 1 of the corresponding GSF. Correlation between parameter estimates causes nonmonotonic oscillatory patterns in GSF. The authors´ results show that GSF are a useful tool in the analysis of PK data
Keywords :
parameter estimation; patient treatment; physiological models; sensitivity analysis; discrete-time sparse data; estimated model parameters; generalized sensitivity functions; metabolic studies; observed system output patterns; pharmacokinetic data information content analysis; physiological processes; step changes; Biomedical engineering; Biomedical informatics; Context modeling; Data analysis; Information analysis; Mathematical model; Parameter estimation; Predictive models; Sensitivity analysis; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
ISSN :
1094-687X
Print_ISBN :
0-7803-6465-1
Type :
conf
DOI :
10.1109/IEMBS.2000.900768
Filename :
900768
Link To Document :
بازگشت