T1DM model parameters adjustment based on PK, PD and CGM data

Tárnik, Marían

doi:10.1109/ines.2015.7329728

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…The effectiveness of the identification process was measured with a well-known quality metric called root mean square error (RMSE) [11]. We calculated it in vector form as follows:…”

Section: Resultsmentioning

confidence: 99%

Nonlinear identification of glucose absorption related to Diabetes Mellitus

Eigner

Koppany

Pausits

et al. 2017

2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES)

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In case of biomedical researches we often have to deal with complicated biological phenomenons, which are usually described with complex mathematical models. In most cases these mathematical models and the systems to be modelled are also nonlinear. The appropriate adjustment of the parameters of these models is always a problem which is hard to be solved. To work with such complex models is essential in many research fields and application areas e.g. in personalized medicine or by the control of physiological processes. Although there are many identification techniques available, there is no general or "oven-ready" solution in cases where the mathematical model describing the dynamics of the physiological processes is highly nonlinear. One of our aims was to develop a simple, userfriendly and flexible identification framework which supports the identification of complex, nonlinear mathematical models. The performance of the method can be measured by simple metric. On the other hand, our goal was to successfully realize the identification framework in case of glucose absorption models, which are essential in our future work in order to validate the performance of advanced control algorithms. Our results show that the nonlinear identification framework performed well, since the predefined requirements were satisfied in all cases.

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“…The effectiveness of the identification process was measured with a well-known quality metric called root mean square error (RMSE) [11]. We calculated it in vector form as follows:…”

Section: Resultsmentioning

confidence: 99%

Nonlinear identification of glucose absorption related to Diabetes Mellitus

Eigner

Koppany

Pausits

et al. 2017

2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES)

View full text Add to dashboard Cite

show abstract

“…where N is the number of samples of the measured data. The last metric measures the fitness of the model by using the simple fit metric (SFM) [18] in percentage. The SFM can be calculated in the following way:…”

Section: Identification Of the Modelmentioning

confidence: 99%

Nonlinear identification of a tumor growth model for validating cancer treatments

Eigner

Szögi

Pausits

et al. 2017

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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In case of physiological related researches the appropriate adjustment of the parameters of the mathematical models describing biological phenomenons is a crucial issue. These models are essential in many research field such as the personalized health care or the control of physiological processes. Despite the available identification techniques there is no general solution in those cases where the mathematical model is given, but highly nonlinear in order to capture the main dynamical attitude of the physiological processes to be described. One of our aims was to develop such a general nonlinear identification framework which is flexible, can be easily used and supports the identification of these kind of models. We defined different metrics to measure the performance of the developed system. From the other hand, our goal was to successfully realize the identification framework in case of tumor growth beside anti-angiogenic treatment which is essential in our future work in order to validate the performance of advanced control algorithms. The results show that the nonlinear identification framework performed well in this case, since the predefined requirements from the applied metrics points of view were satisfied in all cases.

show abstract

T1DM model parameters adjustment based on PK, PD and CGM data

Cited by 2 publications

References 17 publications

Nonlinear identification of glucose absorption related to Diabetes Mellitus

Nonlinear identification of glucose absorption related to Diabetes Mellitus

Nonlinear identification of a tumor growth model for validating cancer treatments

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