Almásy, M Gy and Hörömpő, A and Kiss, Dániel and Kertész, Gábor (2022) A review on modeling tumor dynamics and agent reward functions in reinforcement learning based therapy optimization. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 43 (6). pp. 6939-6946. ISSN 1064-1246 10.3233/JIFS-212351

Text Almasy_6939_33041882_z.pdf Restricted to Registered users only Download (126kB) | Request a copy

Abstract

Revolutionary changes of deep reinforcement learning are leading to high-performing intelligent solutions in multiple fields, including healthcare. At the moment, chemotherapy and radiotherapy are common types of treatments for cancer, however, both therapies are usually radical procedures with undesirable side effects. There is an increasing number of evidence that patient-based optimal schedule has a significant impact in increasing efficiency and survival, and reducing side effects during both therapies. To apply artificial intelligence in therapy optimization, an adequate model of tumor growth incorporating the effect of the treatment is mandatory. A method on training a controller for dosage and scheduling, reinforcement learning can be applied, where a well-chosen agent rewarding function is key to achieve optimal behavior. In this survey paper, some selected tumor growth models, reinforcement learning based solutions and especially agent reward functions are reviewed and compared, providing a summary on state of the art approaches.

Item Type:	Article
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Laboratory of Parallel and Distributed Systems
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	25 Jan 2023 08:17
Last Modified:	25 Jan 2023 08:17
URI:	https://eprints.sztaki.hu/id/eprint/10451

Update Item