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Content Provider | IEEE Xplore Digital Library |
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Author | Mendyk, A. Dorozynski, P. Jachowicz, R. |
Copyright Year | 2007 |
Description | Author affiliation: Jagiellonian Univ., Cracow (Mendyk, A.; Dorozynski, P.; Jachowicz, R.) |
Abstract | The aim of this study was to use artificial neural networks (ANNs) in the attempt to the identification of model drug release mechanisms from hydrodynamically-balanced systems (HBS). As model drug used was ketoprofen (KT) nonsteroid anti-inflammatory drug. The matrices of HBS were formulated with use of hydrophilic polymers, derivatives of cellulose, carrageens and alginates. Cheminformatics software was applied in order to provide numerical representation of HBS qualitative composition. Artificial neural networks were employed as predictive and data-mining tools. Large initial number of input variables (2659) was reduced using subsequent sensitivity analyses to the 17 inputs. A knowledge-based "context-related" sensitivity analysis was applied in order to cope with cut-off point selection procedure. Additional application of inputs correlation analysis resulted in the final reduction to the 8-input vector. Observations of what was remained as the input variables in the above mentioned 8-input vector was the base of theories formulation concerning KT release mechanism. |
Starting Page | 1871 |
Ending Page | 1876 |
File Size | 1782932 |
Page Count | 6 |
File Format | |
ISBN | 9781424413799 |
ISSN | 10987576 |
DOI | 10.1109/IJCNN.2007.4371243 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2007-08-12 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Artificial neural networks Drugs Polymers Pharmaceutical technology Optimized production technology Input variables Sensitivity analysis Stomach Food technology Software quality |
Content Type | Text |
Resource Type | Article |
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