文章基本信息

标题：LEARNING-BASED CONTROL OF CANCER CHEMOTHERAPY TREATMENT
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作者：Regina Padmanabhan ; Nader Meskin ; Wassim M. Haddad 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：15127-15132
DOI：10.1016/j.ifacol.2017.08.2247
语种：English
出版社：Elsevier
摘要：AbstractThe increasing threat of cancer to human life and the improvement in survival rate of this disease due to effective treatment has promoted research in various related fields. This research has shaped clinical trials and emphasized the necessity to properly schedule cancer chemotherapy to ensure effective and safe treatment. Most of the control methodologies proposed for cancer chemotherapy scheduling treatment are model-based. In this paper, a reinforcement learning (RL)-based, model-free method is proposed for the closed-loop control of cancer chemotherapy drug dosing. Specifically, the Q-learning algorithm is used to develop an optimal controller for cancer chemotherapy drug dosing. Numerical examples are presented using simulated patients to illustrate the performance of the proposed RL-based controller.
关键词：KeywordsReinforcement learningoptimal controlactive drug dosingbiomedical control