Abstract
The use of Hybrid Artificial Intelligent techniques in medicine has increased in recent years. Specifically, one of the main challenges in anesthesia is achieving new controllers capable of automating the drug titration during surgeries. This work deals with the development of a Takagi-Sugeno fuzzy controller to automate the drug infusion for the control of hypnosis in patients undergoing anesthesia. To do that, a combination of Neural Networks and optimization techniques were applied to tune the internal parameters of the fuzzy controller. For the training process, data from 20 patients undergoing surgery were used. Finally, the controller proposed was tested over 16 virtual surgeries. It was concluded that the fuzzy controller was able to meet both clinical and control objectives.
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Acknowledgements
José Manuel Gonzalez-Cava’s research was supported by the Spanish Ministry of Science, Innovation and Universities (http://www.ciencia.gob.es/) under the “Formación de Profesorado Universitario” grant FPU15/03347.
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Gonzalez-Cava, J.M. et al. (2019). Inferring Knowledge from Clinical Data for Anesthesia Automation. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_41
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