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Modeling of drug effect in general closed-loop anesthesia
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In medicine, anesthesia is achieved by administering two interacting drugs. Nowadays,the Depth of Anesthesia can be expressed by the Bispectral Index Scale, which ismeasured by an EEG. In order to make automatic closed-loop anesthesia possiblewith the benefits of 1) relieving the anesthesiologist from the hard task of administering optimal drug doses, 2) achieving more consistent drug effects by meansof individualization, and 3) reducing side effects because of the achieved reduced overall drug administration, estimating accurate models of the effect of drug doses onthe Depth of Anesthesia is essential.The model used was a minimally parametrized PharmacoKinetic-Pharmaco Dynamic Wiener model. The parameters of the model were estimated using an Extended Kalman Filter, whose parameters were tuned manually. The model and filter were tested on new data from both the University of Porto and the University of Brescia.The unit of the reference data set from Porto was unknown, so in order to use the scale-dependent model an educated guess was made to convert the other data sets to a reasonable scale. Furthermore, the data from Brescia was incomplete, which could only partly be remediated. Similar tracking performances were obtained when using the new data sets compared to the reference data, however, either relatively constant estimates, or different parameter estimates for similar conditions, were typically obtained. This questions the validity of the model used and if the parameters foundcan be trusted. Therefore, the replication of the procedure on other complete data,and the comparison with the application of other models on the studied data, is subject for future research.

Place, publisher, year, edition, pages
2017. , 52 p.
Series
IT, 17002
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-322249OAI: oai:DiVA.org:uu-322249DiVA: diva2:1096376
Educational program
Master Programme in Computer Science
Supervisors
Examiners
Available from: 2017-05-17 Created: 2017-05-17 Last updated: 2017-05-17Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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