Drug dosing for cancer therapy: A stochastic model predictive control perspective

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dc.contributor.authorAndrés Hernández-Rivera
dc.contributor.authorPablo Velarde
dc.contributor.authorAscensión Zafra‐Cabeza
dc.contributor.authorJ. M. Maestre
dc.coverage.spatialBolivia
dc.date.accessioned2026-03-22T19:43:44Z
dc.date.available2026-03-22T19:43:44Z
dc.date.issued2025
dc.description.abstractStochastic 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.
dc.identifier.doi10.1016/j.jtbi.2025.112255
dc.identifier.urihttps://doi.org/10.1016/j.jtbi.2025.112255
dc.identifier.urihttps://andeanlibrary.org/handle/123456789/77766
dc.language.isoen
dc.publisherElsevier BV
dc.relation.ispartofJournal of Theoretical Biology
dc.sourceUniversidad de Sevilla
dc.subjectDosing
dc.subjectPerspective (graphical)
dc.subjectCancer therapy
dc.subjectCancer
dc.subjectCancer drugs
dc.subjectMedicine
dc.subjectIntensive care medicine
dc.subjectComputer science
dc.titleDrug dosing for cancer therapy: A stochastic model predictive control perspective
dc.typearticle

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