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Comparison of artificial neural network and anfis for parameter estimation of a tumor model
Cyber-medical systems are revolutionizing medicine. Mathematical therapy optimization is
one of the main pioneering results of cyber-medical system approach. Mathematical therapy …
one of the main pioneering results of cyber-medical system approach. Mathematical therapy …
Tumor model parameter estimation for therapy optimization using artificial neural networks
Therapy optimization and personalization in cancer treatment requires reliable mathematical
models. A key issue in personalization is the identification of the model parameters. We …
models. A key issue in personalization is the identification of the model parameters. We …
Parameter estimation from realistic experiment scenario using artificial neural networks
One of the promising directions in future medicine is the optimization of therapies based on
mathematical and engi-neering methods, with which the treatment can be personalized. In …
mathematical and engi-neering methods, with which the treatment can be personalized. In …
Modeling of tumor growth incorporating the effect of pegylated liposomal doxorubicin
Modeling tumor growth is a fundamental step in the design of automated, optimal, patient
specific therapies. We extend our tumor growth model created to describe the effect of an …
specific therapies. We extend our tumor growth model created to describe the effect of an …
Performance of the SAEM and FOCEI algorithms in the open‐source, nonlinear mixed effect modeling tool nlmixr
The free and open‐source package nlmixr implements pharmacometric nonlinear mixed
effects model parameter estimation in R. It provides a uniform language to define …
effects model parameter estimation in R. It provides a uniform language to define …
Parameter identification of a tumor model using artificial neural networks
Mathematical models of tumor growth and the effect of therapy is fundamental for
personalizing and optimizing anticancer therapies. The aim of the research is to provide a …
personalizing and optimizing anticancer therapies. The aim of the research is to provide a …
Clinical study design strategies to mitigate confounding effects of time‐dependent clearance on dose optimization of therapeutic antibodies
Time‐dependent pharmacokinetics (TDPK) is a frequent confounding factor that misleads
exposure‐response (ER) analysis of therapeutic antibodies, where a decline in clearance …
exposure‐response (ER) analysis of therapeutic antibodies, where a decline in clearance …
Comparison of Michaelis-Menten kinetics modeling alternatives in cancer chemotherapy modeling
Model-based optimization and personalization of tumor therapies require tumor growth
models that reliably describe the effect of the drug used during the therapy. A key …
models that reliably describe the effect of the drug used during the therapy. A key …
Metaheuristics for pharmacometrics
Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to
tackle general purpose optimization problems. Nature‐inspired metaheuristic algorithms is a …
tackle general purpose optimization problems. Nature‐inspired metaheuristic algorithms is a …
Model-Informed Medical Technology Development: A simulation study to evaluate the impact of model-based clinical study design and analysis on effect size …
MM Carvalho Lima Vieira Araujo - 2024 - diva-portal.org
Randomisedcontrolledtrials (RCT) areconsideredthegoldstandardforassessing the efficacy
and safety of medical interventions. However, RCTs face unique challenges when applied to …
and safety of medical interventions. However, RCTs face unique challenges when applied to …