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
Show affiliations +
Journal:
Logic Journal of the IGPL

ISSN: 1367-0751,  1368-9894

Year of publication: 2021

Volume: 29

Issue: 2

Pages: 236-250

Type: Article

DOI: 10.1093/JIGPAL/JZAA049 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Logic Journal of the IGPL

Sustainable development goals

Abstract

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.

Bibliographic References

  • Ye, (2020), Science of the Total Environment, 699, pp. 134279, 10.1016/j.scitotenv.2019.134279
  • Cenamor, (2017), Expert Systems with Applications, 69, pp. 1, 10.1016/j.eswa.2016.10.030
  • Garrido, (2017), Computer Science and Information Systems, 14, pp. 1, 10.2298/CSIS150410029G
  • Parveen, (2020), Chemical Engineering Communications, 207, pp. 213, 10.1080/00986445.2019.1578757
  • Chanamool, (2016), Applied Soft Computing, 43, pp. 441, 10.1016/j.asoc.2016.01.007
  • Nelson, (2019), NPJ Digital Medicine, 2, pp. 1, 10.1038/s41746-019-0103-3
  • Bahado-Singh, (2020), Brain Research, 1726, pp. 146510, 10.1016/j.brainres.2019.146510
  • de Bruin, (2016), Artificial Intelligence in Medicine, 69, pp. 33, 10.1016/j.artmed.2016.04.005
  • Mayro, (2020), Eye, 34, pp. 1, 10.1038/s41433-019-0577-x
  • Marrero, (2017), Journal of Clinical Monitoring and Computing, 31, pp. 319, 10.1007/s10877-016-9868-y
  • Mendez, (2016), Control Engineering Practice, 46, pp. 1, 10.1016/j.conengprac.2015.09.009
  • Gonzalez-Cava, (2018), Complexity., 2018, pp. 9012720, 10.1155/2018/9012720
  • Mendez, (2018), Artificial Intelligence in Medicine, 84, pp. 159, 10.1016/j.artmed.2017.12.005
  • Gonzalez-Cava, (2019), Lecture Notes in Computer Science, 11734, pp. 480, 10.1007/978-3-030-29859-3_41
  • Casteleiro-Roca, (2018), Neural Computing and Applications, 84, pp. 159
  • Jove, (2019), Logic Journal of IGPL, 27, pp. 189, 10.1093/jigpal/jzy032
  • Casteleiro-Roca, (2017), Sensors, 17, pp. 179, 10.3390/s17010179
  • Shander, (2018), Anesthesia and Analgesia, 126, pp. 705, 10.1213/ANE.0000000000002383
  • Hund, (2016), Journal of Medical Systems, 40, pp. 281, 10.1007/s10916-016-0641-z
  • Martín-Mateos, (2016), Computers in Biology and Medicine, 75, pp. 173, 10.1016/j.compbiomed.2016.06.007
  • Guignard, (2006), Best Practice & Research. Clinical Anaesthesiology, 20, pp. 161, 10.1016/j.bpa.2005.09.002
  • Cowen, (2015), Anaesthesia, 70, pp. 828, 10.1111/anae.13018
  • von Dincklage, (2015), Anaesthesist, 64, pp. 758, 10.1007/s00101-015-0080-0
  • Logier, (2010), 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1194, 10.1109/IEMBS.2010.5625971
  • Szental, (2015), British Journal of Anaesthesia, 114, pp. 640, 10.1093/bja/aeu411
  • Castro, (2017), Journal of Clinical Monitoring and Computing, 31, pp. 851, 10.1007/s10877-016-9905-x
  • Singh, (1999), pp. 31
  • Zhang, (2019), Journal of Biomedical Informatics, 99, pp. 103294, 10.1016/j.jbi.2019.103294
  • Cyran, (2013), Smart Innovations, Systems and Technologies, 13, pp. 379, 10.1007/978-3-642-28699-5_15
  • Zhang, (2016), Annals of Translation Medicine, 4, pp. 218, 10.21037/atm.2016.03.37
  • Kotsiantis, (2013), Artificial Intelligence Review, 39, pp. 261, 10.1007/s10462-011-9272-4
  • Collins, (2002), Machine Learning, 48, pp. 253, 10.1023/A:1013912006537
  • Probst, (2019), Wiley Interdisciplinary Reviews. Data Mining Knowledge Discovery, 9, pp. e1301, 10.1002/widm.1301
  • Lu, (2009), IEEE Transactions on Multimedia, 11, pp. 1289, 10.1109/TMM.2009.2030632
  • Seiffert, (2010), IEEE Transactions on Systems, Man, and Cybernetics. Part A: Systems Humans, 40, pp. 185, 10.1109/TSMCA.2009.2029559
  • Sharma, (2015), Neurocomputing, 151, pp. 207, 10.1016/j.neucom.2014.09.051
  • Hosmer, (2013), Applied Logistic Regression, 10.1002/9781118548387
  • Gueth, (2013), Physics in Medicine and Biology, 58, pp. 4563, 10.1088/0031-9155/58/13/4563
  • Huang, (2017), PLoS One, 12, pp. e0186906, 10.1371/journal.pone.0186906
  • Galley, (2004), British Journal of Anaesthesia, pp. 623
  • Ledowski, (2019), British Journal of Anaesthesia, 123, pp. 312, 10.1016/j.bja.2019.03.024
  • Yan, (2017), BJA British Journal of Anaesthesia, 118, pp. 635, 10.1093/bja/aex061
  • Chanques, (2017), British Journal of Anaesthesia, 119, pp. 812, 10.1093/bja/aex210
  • Gruenewald, (2015), pp. 480
  • Jeanne, (2012), Journal of Clinical Monitoring and Computing, 26, pp. 289, 10.1007/s10877-012-9354-0
  • Kommula, (2019), pp. 57
  • Le Gall, (2019), pp. 35
  • Boselli, (2016), Journal of Clinical Monitoring and Computing, 30, pp. 977, 10.1007/s10877-015-9802-8