Explicit approximation of stochastic optimal feedback control for combined therapy of cancer

In this paper, a tractable methodology is proposed to approximate stochastic optimal feedback treatment in the context of mixed immuno-chemo therapy of cancer. The method uses a fixed-point value iteration that approximately solves a stochastic dynamic programming-like equation. It is in particular...

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Published in:arXiv.org 2020-10
Main Author: Alamir, Mazen
Format: Article
Language:English
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Cancer
Chemotherapy
Clustering
Dynamic programming
Feedback control
Iterative methods
Machine learning
Parameter uncertainty
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description In this paper, a tractable methodology is proposed to approximate stochastic optimal feedback treatment in the context of mixed immuno-chemo therapy of cancer. The method uses a fixed-point value iteration that approximately solves a stochastic dynamic programming-like equation. It is in particular shown that the introduction of a variance-related penalty in the latter induces better results that cope with the consequences of softening the health safety constraints in the cost function. The convergence of the value function iteration is revisited in the presence of the variance related term. The implementation involves some Machine Learning tools in order to represent the optimal function and to perform complexity reduction by clustering. Quantitative illustration is given using a commonly used model of combined therapy involving twelve highly uncertain parameters.
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subjects Cancer
Chemotherapy
Clustering
Dynamic programming
Feedback control
Iterative methods
Machine learning
Parameter uncertainty
title Explicit approximation of stochastic optimal feedback control for combined therapy of cancer
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