A Machine Learning Based System for Analgesic Drug Delivery View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-08-23

AUTHORS

Jose M. Gonzalez-Cava , Rafael Arnay , Juan Albino Méndez Pérez , Ana León , María Martín , Esteban Jove-Perez , José Luis Calvo-Rolle , Jose Luis Casteleiro-Roca , Francisco Javier de Cos Juez

ABSTRACT

Monitoring pain and finding more efficient methods for analgesic administration during anaesthesia is a challenge that attracts the attention of both clinicians and engineers. This work focuses on the application of Machine Learning techniques to assist the clinicians in the administration of analgesic drug. The problem will consider patients undergoing general anaesthesia with intravenous drug infusion. The paper presents a preliminary study based on the use of the signal provided by an analgesia monitor, the Analgesia Nociception Index (ANI) signal. One aim of this research is studying the relation between ANI monitor and the changes in drug titration made by anaesthetist. Another aim is to propose an intelligent system that provides decisions on the drug infusion according to the ANI evolution. To do that, data from 15 patients undergoing cholecystectomy surgery were analysed. In order to establish the relationship between ANI and the analgesic, Machine Learning techniques have been introduced. After training different types of classifier and testing the results with cross validation method, it has been demonstrated that a relation between ANI and the administration of remifentanil can be found. More... »

PAGES

461-470

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-67180-2_45

DOI

http://dx.doi.org/10.1007/978-3-319-67180-2_45

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091293303


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