A novel evolutionary drug scheduling model in cancer chemotherapy
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AbstractIn this paper, we introduce a modified optimal control model of drug scheduling in cancer chemotherapy and a new adaptive elitist-population-based genetic algorithm (AEGA) to solve it. Working closely with an oncologist, we first modify the existing model, because its equation for the cumulative drug toxicity is inconsistent with medical knowledge and clinical experience. To explore multiple efficient drug scheduling policies, we propose a novel variable representation-a cycle-wise representation, and modify the elitist genetic search operators in the AEGA. The simulation results obtained by the modified model match well with the clinical treatment experiences, and can provide multiple efficient solutions for oncologists to consider. Moreover, it has been shown that the evolutionary drug scheduling approach is simple, and capable of solving complex cancer chemotherapy problems by adapting mul-timodal versions of evolutionary algorithms.
All Author(s) ListLiang Y, Leung KS, Mok TSK
Journal nameIEEE Transactions on Information Technology in Biomedicine
Volume Number10
Issue Number2
Pages237 - 245
LanguagesEnglish-United Kingdom
Keywordsdrug scheduling model; genetic algorithms
Web of Science Subject CategoriesComputer Science; Computer Science, Information Systems; COMPUTER SCIENCE, INFORMATION SYSTEMS; Computer Science, Interdisciplinary Applications; COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS; Mathematical & Computational Biology; MATHEMATICAL & COMPUTATIONAL BIOLOGY; Medical Informatics; MEDICAL INFORMATICS

Last updated on 2020-17-10 at 00:35