A novel evolutionary drug scheduling model in cancer chemotherapy.
In 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 multimodal versions of evolutionary algorithms.
Liang
Yong
Y
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR. yliang@cse.cuhk.edu.hk
Leung
Kwong-Sak
KS
Mok
Tony Shu Kam
TS
D000465:Algorithms; D000970:Antineoplastic Agents / Q000008:administration & dosage*; D001185:Artificial Intelligence; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D004334:Drug Administration Schedule; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D009369:Neoplasms / Q000188:drug therapy*
D000970:Antineoplastic Agents