Comparacion de estrategias de control predictivo estocástico no lineal aplicadas a la quimioterapia
[EN] Mathematical models of biomedical systems can help practitioners design safer and more effective drug administration cycles. To achieve this goal, the mathematical model of tumoral growth and the impact of chemotherapy are used in the decision-making process. However, biomedical systems are pro...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/222698 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/222698 |
| Access Level: | acceso abierto |
| Palabra clave: | Predictive control Stochastic optimal control Pharmacokinetics and drug delivery Nonlinear predictive control Control predictivo Control óptimo estocástico Farmacocinética y administración de fármacos Control predictivo no lineal |
| Sumario: | [EN] Mathematical models of biomedical systems can help practitioners design safer and more effective drug administration cycles. To achieve this goal, the mathematical model of tumoral growth and the impact of chemotherapy are used in the decision-making process. However, biomedical systems are prone to a high degree of uncertainty, not only from measurement errors but also from unmodeled dynamics of the system and interpatient variability. To address this issue, probabilistic constraints have been applied to the control of the drug administration process, making it more robust against disturbances. This work compares a nonlinear and a linearized version of the stochastic formulations of the model predictive control. Both algorithms enhance treatment efficacy and safety, with differences in conservativeness and computational cost. |
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