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Automating the drug scheduling with different toxicity clearance in cancer chemotherapy via evolutionary computation
Date Issued
2006
Publisher
Association for Computing Machinery (ACM)
ISBN
1595931864
978-159593186-3
Citation
GECCO 2006 - Genetic and Evolutionary Computation Conference, 2006, vol. 2, pp. 1705 - 1712
Type
Conference Paper
Abstract
The 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. Copyright 2006 ACM.
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