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AuthorPadmanabhan, Regina
AuthorMeskin, Nader
AuthorHaddad, Wassim M.
Available date2020-09-24T08:11:56Z
Publication Date2017
Publication NameMathematical Biosciences
ResourceScopus
ISSN255564
URIhttp://dx.doi.org/10.1016/j.mbs.2017.08.004
URIhttp://hdl.handle.net/10576/16276
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. 1 2017 Elsevier Inc.
SponsorThis publication was made possible by the GSRA grant No. GSRA1-1-1128-13016 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors.
Languageen
PublisherElsevier Inc.
SubjectActive drug dosing
Chemotherapy control
Reinforcement learning
TitleReinforcement learning-based control of drug dosing for cancer chemotherapy treatment
TypeArticle
Pagination20-Nov
Volume Number293
dc.accessType Abstract Only


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