Consensus-Fuzzy Ecological Joint Therapy for Multitumor Populations

• Published in: IEEE transactions on fuzzy systems Vol. 32; no. 3; pp. 699 - 709
• Main Authors: Sun, Jiayue, Yan, Ying, Zhang, Huaguang, Shao, Mingrui
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive dynamic programming (ADP); Costs; Dynamic programming; generalized fuzzy hyperbolic model (GFHM); Hamiltonian functions; Iterative algorithms; Iterative methods; Multiagent systems; multiple agents; optimal joint therapy; Populations; Social factors; Statistics; Topology; tumor cell; Tumors
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2023.3305007

This article investigates a class of optimal joint therapeutic regimens based on adaptive dynamic programming (ADP) and generalized fuzzy hyperbolic model (GFHM) for multiple tumor populations subject to immune effects as well as multiple drugs. Obtaining a model of the abnormal proliferation process of single tumor cells with multiple interventions is then the first step to conducting the treatment. In this article, the trajectories of healthy and unhealthy systems are obtained by constructing a virtual affine proliferation model. A strong connected trace of multiple agents is further developed by constructing a Hamiltonian function of the drug delivery cost of the unhealthy system. ADP and GFHM are gathered as consensus-fuzzy policy iterative algorithms, in which convergence and stability are ensured and the optimal dosing scheme achieves tumor cell inhibition.