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Adaptive Predictive Control Based Therapy of Bone Marrow Cancer

机译:基于自适应预测控制的骨髓癌治疗

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This paper starts by reviewing the mathematical model for tumor growth as well as the pharmacokinetics and pharmacodynamics models of the drug, so that the therapy can be as close as possible to reality. A Nonlinear Model Predictive Control algorithm ( NMPC ) is used to find the optimal drug dose, in order to reduce the bone marrow tumor density. The Recursive Least Squares algorithm is used to learn the parameters of the tumor growth model, in order to obtain an adaptive NMPC strategy. This control strategy is applied to a bone microenvironment model to schedule a therapy for reducing tumor density.
机译:本文首先回顾了肿瘤生长的数学模型以及该药物的药代动力学和药效动力学模型,以便使该疗法尽可能接近现实。为了降低骨髓肿瘤密度,使用了非线性模型预测控制算法(NMPC)来找到最佳药物剂量。递推最小二乘算法用于学习肿瘤生长模型的参数,以获得自适应的NMPC策略。将该控制策略应用于骨骼微环境模型,以安排降低肿瘤密度的疗法。

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