Inferring Knowledge from Clinical Data for Anesthesia Automation

• Gonzalez-Cava, Jose M. ^[1] ; Iván Castilla-Rodríguez ^[1] ; José Antonio Reboso ^[2] ; Ana León ^[2] ; Martín, María ^[2] ; Esteban Jove-Pérez ^{[1] [3]} ; José Luis Calvo-Rolle ^[3] ; Méndez-Pérez, Juan Albino ^[1]
  1. [1] Universidad de La Laguna

     Universidad de La Laguna

     San Cristóbal de La Laguna, España

     
  2. [2] Hospital Universitario de Canarias

     Hospital Universitario de Canarias

     San Cristóbal de La Laguna, España

     
  3. [3] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     

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• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 480-491
• Idioma: inglés
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  • 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.