Part of #Reinforcement Learning-Based Control of Chronic Myelogenous Leukemia (CML)# :
Publishing year : 2012
Conference : First National Conference on Electrical Engineering in Isfahan
Number of pages : 5
Abstract: In this paper, we find treatment regimens that minimize cancer cell count by using a drug for a patient who has Chronic Myelogenous Leukemia (CML). CML is a disease of the hematopoietic stem cells. The goal of this paper is to develop an effective drug-schedule to reduce the number of cancer cells in a timely manner. To achieve this goal, a Rational Learning Learning (RL), which is the ooo of the best uncontrolled machine learning algorithms, is applied. Because RL does not need any environmental model, i.e. It is model-free; It has absorbed the interests of recent years, especially in medical applications. The performance of the proposed approach is evaluated by simulating a chemotherapeutic model of drug dosage of CML therapy. Simulation results show the feasibility of the proposed method.