Drug dosing for cancer therapy: A stochastic model predictive control perspective
Andrés Hernández-Rivera1, Pablo Velarde2, Ascensión Zafra-Cabeza1
1Department of System and Automation Engineering, University of Seville, Spain.
Abstract:
Stochastic Model Predictive Control (SMPC) is an effective decision-making method in applications where uncertainties play a significant role. This work introduces a non-linear formulation of SMPC specifically designed for cancer therapy. The proposed method considers the stochastic nature of tumor growth, non-linear dynamics, and a potential side effect of the treatment. Through one-year simulations, the results showcase the effectiveness of this strategy in controlling drug dosing.
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