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Learning Bayesian Network to Predict Group Emotion in Kindergarten by Evolutionary Computation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

An Intelligent Model to Predict ANI in Patients Undergoing General Anesthesia

Authors : Esteban Jove, Jose M. Gonzalez-Cava, José Luis Casteleiro-Roca, Juan Albino Méndez Pérez, José Luis Calvo-Rolle, Francisco Javier de Cos Juez

Published in: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Publisher: Springer International Publishing

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Abstract

One of the main challenges in anesthesia is the proposal of safe and efficient methods to administer drugs to regulate the pain that the patient is sufffering during the surgical process. First steps towards this objective is the proposal of adequate indexes that correlate well with analgesia. One of the most promising index is ANI (Antinociception Index). This research focuses on the modelling of the ANI response in patients undergoing general anesthesia with intravenous drug infusion. The aim is to predict the ANI response in terms of the analgesic infusion rate. For this a model based on intelligent regression techniques is proposed. To create the model, it has been checked Artificial Neural Networks (ANN) and Support Vector Regression (SVR). Results were validated using data from patients in the operating room. The measured performance attest for the potential of the proposed technique.

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previous chapter Data Mining Techniques for the Estimation of Variables in Health-Related Noisy Data
next chapter Coupling the PAELLA Algorithm to Predictive Models
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Metadata
Title
An Intelligent Model to Predict ANI in Patients Undergoing General Anesthesia
Authors
Esteban Jove
Jose M. Gonzalez-Cava
José Luis Casteleiro-Roca
Juan Albino Méndez Pérez
José Luis Calvo-Rolle
Francisco Javier de Cos Juez
Copyright Year
2018
Book
International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Print ISBN: 978-3-319-67179-6

Electronic ISBN: 978-3-319-67180-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-67180-2_48

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