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- W4281569709 abstract "Cyber-medical systems are revolutionizing medicine. Mathematical therapy optimization is one of the main pioneering results of cyber-medical system approach. Mathematical therapy optimization is based on a mathematical model. A reliable model with personalized model parameters is crucial for generating optimal treatment. We discuss algorithms for estimation of tumor model parameters using artificial intelligence methods. We use in silico experiments to create a large set of training data in a span that covers real-life scenarios. Artificial intelligence-based systems may provide reliable estimation results in medical applications. In this research two different estimators will be introduced based on artificial neural network and fuzzy methods. The training data are generated on known parameter intervals, taking into consideration the experimental setup we use to validate our results. The estimated parameters can be used to track the change of the parameters and personalize the model for optimization algorithms. The two estimators are compared and shown that they compensate the weaknesses of each other." @default.
- W4281569709 created "2022-05-27" @default.
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- W4281569709 date "2022-03-02" @default.
- W4281569709 modified "2023-09-27" @default.
- W4281569709 title "Comparison of artificial neural network and ANFIS for parameter estimation of a tumor model" @default.
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- W4281569709 doi "https://doi.org/10.1109/sami54271.2022.9780819" @default.
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