Nonlinear identification of a tumor growth model for validating cancer treatments

György Eigner, Gábor Szögi, Péter Pausits, I. Rudas, L. Kovács

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3159-3163
Number of pages5
Volume2017-January
ISBN (Electronic)9781538616451
DOIs
Publication statusPublished - Nov 27 2017
Event2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 - Banff, Canada
Duration: Oct 5 2017Oct 8 2017

Other

Other2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
CountryCanada
CityBanff
Period10/5/1710/8/17

Fingerprint

Nonlinear Identification
Oncology
Tumor Growth
Growth Model
Tumors
Identification (control systems)
Cancer
Mathematical models
Health care
Mathematical Model
Metric
General Solution
Healthcare
Control Algorithm
Adjustment
Requirements
Model
Framework

Keywords

  • Identification
  • Nonlinear least mean square method
  • Tumor growth
  • Tumor model identification

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Control and Optimization

Cite this

Eigner, G., Szögi, G., Pausits, P., Rudas, I., & Kovács, L. (2017). Nonlinear identification of a tumor growth model for validating cancer treatments. In 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 (Vol. 2017-January, pp. 3159-3163). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2017.8123113

Nonlinear identification of a tumor growth model for validating cancer treatments. / Eigner, György; Szögi, Gábor; Pausits, Péter; Rudas, I.; Kovács, L.

2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 3159-3163.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Eigner, G, Szögi, G, Pausits, P, Rudas, I & Kovács, L 2017, Nonlinear identification of a tumor growth model for validating cancer treatments. in 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 3159-3163, 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, Banff, Canada, 10/5/17. https://doi.org/10.1109/SMC.2017.8123113
Eigner G, Szögi G, Pausits P, Rudas I, Kovács L. Nonlinear identification of a tumor growth model for validating cancer treatments. In 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. Vol. 2017-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 3159-3163 https://doi.org/10.1109/SMC.2017.8123113
Eigner, György ; Szögi, Gábor ; Pausits, Péter ; Rudas, I. ; Kovács, L. / Nonlinear identification of a tumor growth model for validating cancer treatments. 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 3159-3163
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