Publication: PARAMETER ESTIMATION of A SIMPLE, REALISTIC STOCHASTIC MODEL of GASTRIC EMPTYING of PELLETS under FASTING CONDITIONS
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Issued Date
2021-09-01
Resource Type
ISSN
02183390
Other identifier(s)
2-s2.0-85113502271
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Mahidol University
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SCOPUS
Bibliographic Citation
Journal of Biological Systems. Vol.29, No.3 (2021), 561-576
Suggested Citation
Pairote Satiracoo, Andrea De Gaetano PARAMETER ESTIMATION of A SIMPLE, REALISTIC STOCHASTIC MODEL of GASTRIC EMPTYING of PELLETS under FASTING CONDITIONS. Journal of Biological Systems. Vol.29, No.3 (2021), 561-576. doi:10.1142/S0218339021500091 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/75592
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Title
PARAMETER ESTIMATION of A SIMPLE, REALISTIC STOCHASTIC MODEL of GASTRIC EMPTYING of PELLETS under FASTING CONDITIONS
Author(s)
Abstract
This study is aimed to develop a parameter estimation procedure for a stochastic model of gastric emptying of pellets under fasting conditions of healthy subjects that represent the irregular decrements of gastric contents after administration of pellets. An algorithm for the identification of experimental subjects into subgroups based on their gastric emptying rates has been proposed. The parameter estimation procedure was performed with observational data of gastric emptying profiles of 19 subjects from the existing literature. The identification algorithm was validated through comparison against the original results, showing that seven subjects indicated slow gastric emptying and 12 subjects indicated fast emptying. After excluding one subject with gastric stasis, the optimal results obtained from this study classified subjects into three subgroups: two subjects with slow, five subjects with intermediate and 11 subjects with fast gastric emptying. The proposed parameter estimation procedure of the stochastic model is relatively easy to implement and can be used to analyze real patients' data. The algorithm for identification of the gastric emptying dynamics in experimental subjects could provide useful information for further investigations of long-term energy balance forecasting.