Automating the drug scheduling with different toxicity clearance in cancer chemotherapy via evolutionary computation
Refereed conference paper presented and published in conference proceedings


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AbstractThe toxicity of an anticancer drug is cleared from the body by different processes, including saturable metabolic and nonsaturable renal-excretion pathways. According to the principles of toxicokinetics, we propose a new anticancer drug scheduling model with different toxic elimination processes in this paper. We also present a sophisticated automating drug scheduling approach based on evolutionary computation and computer modeling. To explore multiple efficient drug scheduling policies, we use a multimodal optimization algorithm - adaptive elitist-population based genetic algorithm (AEGA) to solve the new model, and discuss the situation of multiple optimal solutions under different parameter settings. The simulation results obtained by the new model match well with the clinical treatment experience, and can provide much more drug scheduling policies for a doctor to choose depending on the particular conditions of the patients.
All Author(s) ListLiang Y, Lueng KS, Mok TSK
Name of Conference8th Annual Genetic and Evolutionary Computation Conference
Start Date of Conference08/07/2006
End Date of Conference12/07/2006
Place of ConferenceSeattle
Country/Region of ConferenceUnited States of America
Detailed descriptionorganized by IEEE Congress,
Year2006
Month1
Day1
PublisherASSOC COMPUTING MACHINERY
Pages1705 - 1712
ISBN978-1-59593-186-3
LanguagesEnglish-United Kingdom
Keywordsdrug scheduling model; multimodal optimization algorithm
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Software Engineering

Last updated on 2020-20-10 at 02:08