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An intelligent control strategy for cancer cells reduction in patients with chronic myelogenous leukaemia using the reinforcement learning and considering side effects of the drug
Author(s) -
Noori Amin,
Alfi Alireza,
Noori Ghazaleh
Publication year - 2021
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12655
Subject(s) - drug , computer science , reinforcement learning , medicine , q learning , dose , cancer , reduction (mathematics) , population , regimen , artificial intelligence , pharmacology , surgery , mathematics , environmental health , geometry
Chronic Myelogenous Leukaemia (CML) is a haematopoietic stem cells disease with complex dynamical behaviour. One of the effective factors in treating patients is to determine the appropriate drug dosage. A physician should test the different drug dosages through trial and error in order to find its optimal value. This procedure is normally a time‐consuming and error‐prone task that can even be harmful. The contribution of this paper is to design an intelligent control strategy, which can be used to help physicians, by finding a drug treatment regimen to minimize the number of cancer cells for a CML patient. In this paper, the eligibility traces algorithm and Q‐learning approach are adopted as sub‐optimal methods for progressively reducing the population of cancer cells. In addition, the injected dosage of the drug has improved, compared with previous methods. More importantly, the proposed method is followed by the reduction in side effects of the drug. The advantage of the backward view and the previous states investigation are applied in the Eligibility Traces algorithm. These effects increase the learning procedure and decrease the growth rate of cancer cells and total dosage of the injected drug during the treatment period of time. The proposed strategy mitigates the side effects of the drug on the normal cells.