Machine learning techniques for computer-based decision systems in the operating theatre: application to analgesia delivery

  1. Gonzalez-Cava, Jose M 2
  2. Arnay, Rafael 2
  3. Mendez-Perez, Juan Albino 2
  4. León, Ana 1
  5. Martín, María 1
  6. Reboso, Jose A 1
  7. Jove-Perez, Esteban 4
  8. Calvo-Rolle, Jose Luis 3
  1. 1 Hospital Universitario de Canarias, 38320 La Laguna (Tenerife), Spain
  2. 2 Department of Computer Science and System Engineering, Universidad de La Laguna (ULL), 38200 La Laguna (Tenerife), Spain
  3. 3 Department of Industrial Engineering, Universidade da Coruña, 15405 Coruña, Spain
  4. 4 Department of Computer Science and System Engineering, Universidad de La Laguna (ULL), 38200 La Laguna (Tenerife), Spain and Department of Industrial Engineering, Universidade da Coruña, 15405 Coruña, Spain
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Revista:
Logic Journal of the IGPL

ISSN: 1367-0751,  1368-9894

Año de publicación: 2021

Volumen: 29

Número: 2

Páginas: 236-250

Tipo: Artículo

DOI: 10.1093/JIGPAL/JZAA049 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Logic Journal of the IGPL

Objetivos de desarrollo sostenible

Resumen

Abstract This work focuses on the application of machine learning techniques to assist the clinicians in the administration of analgesic drug during general anaesthesia. Specifically, the main objective is to propose the basis of an intelligent system capable of making decisions to guide the opioid dose changes based on a new nociception monitor, the analgesia nociception index (ANI). Clinical data were obtained from 15 patients undergoing cholecystectomy surgery. By means of an off-line study, machine learning techniques were applied to analyse the possible relationship between the analgesic dose changes performed by the physician due to the hemodynamic activity of the patients and the evolution of the ANI. After training different classifiers and testing the results under cross validation, a preliminary relationship between the evolution of ANI and the dosage of remifentanil was found. These results evidence the potential of the ANI as a promising index to guide the infusion of analgesia.

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