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2018 | OriginalPaper | Buchkapitel

Remifentanil Dose Prediction for Patients During General Anesthesia

verfasst von : Esteban Jove, Jose M. Gonzalez-Cava, José-Luis Casteleiro-Roca, Héctor Quintián, Juan Albino Méndez-Pérez, José Luis Calvo-Rolle, Francisco Javier de Cos Juez, Ana León, María Martín, José Reboso

Erschienen in: Hybrid Artificial Intelligent Systems

Verlag: Springer International Publishing

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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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Titel: Remifentanil Dose Prediction for Patients During General Anesthesia
verfasst von: Esteban Jove
Jose M. Gonzalez-Cava
José-Luis Casteleiro-Roca
Héctor Quintián
Juan Albino Méndez-Pérez
José Luis Calvo-Rolle
Francisco Javier de Cos Juez
Ana León
María Martín
José Reboso
Verlag: Springer International Publishing
Buch: Hybrid Artificial Intelligent Systems
Print ISBN: 978-3-319-92638-4

Electronic ISBN: 978-3-319-92639-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-92639-1_45

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