Evolutionary drug scheduling models with different toxicity metabolism in cancer chemotherapy
Publication in refereed journal


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摘要Through incorporating into Martin's drug scheduling model a toxicity metabolism term, our modified model takes into account the body's ability of recovering from the effect of the drug and successively overcomes two unreasonable problems in Martin's model. Since different drugs have different toxicity metabolism processes, we propose two renewed drug scheduling models with different toxicity metabolism according to kinetics of enzyme-catalyzed chemical reactions. For exploring multiple efficient drug scheduling policies, we use our adaptive elitist-population based genetic algorithm (AEGA) to solve the renewed models, and discuss the situation of multiple optimal solutions under different parameter settings. The simulation results obtained by the renewed models match well with the clinical treatment experience, and can provide much more drug scheduling polices for the doctor to choose depending on the particular conditions of the patients. (c) 2006 Elsevier B.V. All rights reserved.
著者Liang Y, Leung KS, Mok TSK
期刊名稱Applied Soft Computing
出版年份2008
月份1
日期1
卷號8
期次1
出版社ELSEVIER SCIENCE BV
頁次140 - 149
國際標準期刊號1568-4946
電子國際標準期刊號1872-9681
語言英式英語
關鍵詞drug scheduling model; genetic algorithms
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; Computer Science, Interdisciplinary Applications; COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

上次更新時間 2021-28-02 於 00:31