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Remifentanil Dose Prediction for Patients During General Anesthesia

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Hybrid Artificial Intelligent Systems (HAIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10870))

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Abstract

In the anesthesia field there are some challenges, such as achieving new methods to control, and, of course, for reducing the pain suffered for the patients during surgeries. The first steps in this field were focused on obtaining representative measurements for pain measurement. Nowadays, one of the most promiser index is the ANI (Antinociception Index). This research works deals the model for the remifentanil dose prediction for patients undergoing general anesthesia. To do that, a hybrid model based on intelligent techniques is implemented. The model was trained using Support Vector Regression (SVR) and Artificial Neural Networks (ANN) algorithms. Results were validated with a real dataset of patients. It was possible to check the really successful model performance.

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Acknowledgments

Jose M. Gonzalez-Cava’s research was supported by the Spanish Ministry of Education, Culture and Sport (www.mecd.gob.es), under the “Formación de Profesorado” grant FPU15/03347.

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Authors and Affiliations

  1. Department of Industrial Engineering, University of A Coruña, Avda. 19 de febrero s/n, Ferrol, A Coruña, 15495, Spain

    Esteban Jove, José-Luis Casteleiro-Roca, Héctor Quintián & José Luis Calvo-Rolle

  2. Department of Computer Science and System Engineering, Universidad de La Laguna, Avda. Astrof. Francisco Sánchez s/n, S/C de Tenerife, 38200, Spain

    Esteban Jove, Jose M. Gonzalez-Cava, José-Luis Casteleiro-Roca & Juan Albino Méndez-Pérez

  3. Department of Mining Exploitation, University of Oviedo, Calle San Francisco, 1, Oviedo, 33004, Spain

    Francisco Javier de Cos Juez

  4. Hospital Universitario de Canarias, Tenerife, Spain

    Ana León, María Martín & José Reboso

Authors
  1. Esteban Jove
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  2. Jose M. Gonzalez-Cava
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  3. José-Luis Casteleiro-Roca
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  4. Héctor Quintián
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  5. Juan Albino Méndez-Pérez
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  6. José Luis Calvo-Rolle
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  7. Francisco Javier de Cos Juez
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  8. Ana León
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  9. María Martín
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  10. José Reboso
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Corresponding author

Correspondence to Esteban Jove .

Editor information

Editors and Affiliations

  1. Department of Mine Operating and Prospection, University of Oviedo, Oviedo, Spain

    Francisco Javier de Cos Juez

  2. Department of Computer Science, University of Oviedo, Oviedo, Spain

    José Ramón Villar

  3. Department of Computer Science, University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  4. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of A Coruña, A Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    José António Sáez

  7. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Jove, E. et al. (2018). Remifentanil Dose Prediction for Patients During General Anesthesia. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_45

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  • DOI: https://doi.org/10.1007/978-3-319-92639-1_45

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