Complexity
Volume 2018, Article ID 9012720, 15 pages
https://doi.org/10.1155/2018/9012720
A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision-Making Process in Medicine
1Department of Computer Science and System Engineering, Universidad de La Laguna (ULL), San Cristóbal de La Laguna, 38200 Tenerife, Spain
2Hospital Universitario de Canarias, San Cristóbal de La Laguna, Tenerife, Spain
3Department of Industrial Engineering, Universidade da Coruña, Coruña, Spain
Correspondence should be addressed to Jose M. Gonzalez-Cava; se.ude.llu@claznogj
Received 30 November 2017; Revised 16 January 2018; Accepted 23 January 2018; Published 18 February 2018
Academic Editor: José Manuel Andújar
Copyright © 2018 Jose M. Gonzalez-Cava et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Linked References
- J.-C. Pomerol, “Artificial intelligence and human decision making,” European Journal of Operational Research, vol. 99, no. 1, pp. 3–25, 1997. View at Publisher · View at Google Scholar · View at Scopus
- J. Lee, J. Ni, D. Djurdjanovic, H. Qiu, and H. Liao, “Intelligent prognostics tools and e-maintenance,” Computers in Industry, vol. 57, no. 6, pp. 476–489, 2006. View at Publisher · View at Google Scholar · View at Scopus
- P. Holimchayachotikul and K. Leksakul, “Predictive performance measurement system for retail industry using neuro-fuzzy system based on swarm intelligence,” Soft Computing, vol. 21, no. 7, pp. 1895–1912, 2017. View at Publisher · View at Google Scholar · View at Scopus
- I. Cenamor, T. de la Rosa, S. Núñez, and D. Borrajo, “Planning for tourism routes using social networks,” Expert Systems with Applications, vol. 69, pp. 1–9, 2017. View at Publisher · View at Google Scholar
- P. Garrido, J. Barrachina, F. J. Martinez, and F. J. Seron, “Smart tourist information points by combining agents, semantics and AI techniques,” Computer Science and Information Systems, vol. 14, no. 1, pp. 1–23, 2017. View at Publisher · View at Google Scholar · View at Scopus
- Z. Wang and R. S. Srinivasan, “A review of artificial intelligence based building energy use prediction: contrasting the capabilities of single and ensemble prediction models,” Renewable and Sustainable Energy Reviews, vol. 75, pp. 796–808, 2017. View at Publisher · View at Google Scholar · View at Scopus
- B. Robson and S. Boray, “Data-mining to build a knowledge representation store for clinical decision support. Studies on curation and validation based on machine performance in multiple choice medical licensing examinations,” Computers in Biology and Medicine, vol. 73, pp. 71–93, 2016. View at Publisher · View at Google Scholar · View at Scopus
- L. Sarangi, M. N. Mohanty, and S. Patnaik, “Design of ANFIS based e-health care system for cardio vascular disease detection,” in Recent Developments in Intelligent Systems and Interactive Applications, vol. 541 of Advances in Intelligent Systems and Computing, pp. 445–453, Springer International Publishing, Cham, 2017. View at Publisher · View at Google Scholar
- N. Chanamool and T. Naenna, “Fuzzy FMEA application to improve decision-making process in an emergency department,” Applied Soft Computing, vol. 43, pp. 441–453, 2016. View at Publisher · View at Google Scholar
- L. A. G. Celi, R. J. Tang, M. C. Villarroel, G. A. Davidzon, W. T. Lester, and H. C. Chueh, “A clinical database-driven approach to decision support: predicting mortality among patients with acute kidney injury,” Journal of Healthcare Engineering, vol. 2, no. 1, pp. 97–109, 2011. View at Publisher · View at Google Scholar · View at Scopus
- J. S. de Bruin, K.-P. Adlassnig, A. Blacky, and W. Koller, “Detecting borderline infection in an automated monitoring system for healthcare-associated infection using fuzzy logic,” Artificial Intelligence in Medicine, vol. 69, pp. 33–41, 2016. View at Publisher · View at Google Scholar · View at Scopus
- R. Palaniappan, K. Sundaraj, S. Sundaraj, N. Huliraj, and S. S. Revadi, “A telemedicine tool to detect pulmonary pathology using computerized pulmonary acoustic signal analysis,” Applied Soft Computing, vol. 37, pp. 952–959, 2015. View at Publisher · View at Google Scholar · View at Scopus
- J.-L. Casteleiro-Roca, J. L. Calvo-Rolle, J. A. M. Pérez, N. R. Gutiérrez, and F. J. de Cos Juez, “Hybrid intelligent system to perform fault detection on BIS sensor during surgeries,” Sensors, vol. 17, no. 1, article 179, 2017. View at Publisher · View at Google Scholar · View at Scopus
- D. Nauck and R. Kruse, “Obtaining interpretable fuzzy classification rules from medical data,” Artificial Intelligence in Medicine, vol. 16, no. 2, pp. 149–169, 1999. View at Publisher · View at Google Scholar · View at Scopus
- M. Jakubczyk and B. Kaminski, “Fuzzy approach to decision analysis with multiple criteria and uncertainty in health technology assessment,” Annals of Operations Research, vol. 251, no. 1-2, pp. 301–324, 2017. View at Publisher · View at Google Scholar · View at MathSciNet
- J. A. Hazelzet, “Can fuzzy logic make things more clear?” Critical Care, vol. 13, no. 1, 2009. View at Publisher · View at Google Scholar · View at Scopus
- M. J. Gangeh, A. Hashim, A. Giles, L. Sannachi, and G. J. Czarnota, “Computer aided prognosis for cell death categorization and prediction in vivo using quantitative ultrasound and machine learning techniques,” Medical Physics, vol. 43, no. 12, pp. 6439–6454, 2016. View at Publisher · View at Google Scholar · View at Scopus
- Y. Wang, P. Wu, Y. Liu, C. Weng, and D. Zeng, “Learning optimal individualized treatment rules from electronic health record data,” in Proceedings of the 2016 IEEE International Conference on Healthcare Informatics, ICHI '16, pp. 65–71, 2016. View at Publisher · View at Google Scholar · View at Scopus
- M. Dojat, A. Harf, D. Touchard, M. Laforest, F. Lemaire, and L. Brochard, “Evaluation of a knowledge-based system providing ventilatory management and decision for extubation,” American Journal of Respiratory and Critical Care Medicine, vol. 153, no. 3, pp. 997–1004, 1996. View at Publisher · View at Google Scholar · View at Scopus
- H. Ying et al., “A fuzzy discrete event system approach to determining optimal HIV/AIDS treatment regimens,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 4, pp. 663–676, 2006. View at Publisher · View at Google Scholar
- C. M. Salgado, S. M. Vieira, L. F. Mendonça, S. Finkelstein, and J. M. C. Sousa, “Ensemble fuzzy models in personalized medicine: application to vasopressors administration,” Engineering Applications of Artificial Intelligence, vol. 49, pp. 141–148, 2016. View at Publisher · View at Google Scholar · View at Scopus
- A. S. Fialho, F. Cismondi, S. M. Vieira et al., “Fuzzy modeling to predict administration of vasopressors in intensive care unit patients,” in Proceedings of the 2011 IEEE International Conference on Fuzzy Systems, FUZZ '11, pp. 2296–2303, June 2011. View at Publisher · View at Google Scholar · View at Scopus
- A. Marrero, J. A. Méndez, J. A. Reboso, I. Martín, and J. L. Calvo, “Adaptive fuzzy modeling of the hypnotic process in anesthesia,” Journal of Clinical Monitoring and Computing, vol. 31, no. 2, pp. 319–330, 2017. View at Publisher · View at Google Scholar · View at Scopus
- J. Schäublin, M. Derighetti, P. Feigenwinter, S. Petersen-Felix, and A. M. Zbinden, “Fuzzy logic control of mechanical ventilation during anaesthesia,” British Journal of Anaesthesia, vol. 77, no. 5, pp. 636–641, 1996. View at Publisher · View at Google Scholar · View at Scopus
- D. G. Mason, J. J. Ross, N. D. Edwards, D. A. Linkens, and C. S. Reilly, “Self-learning fuzzy control of atracurium-induced neuromuscular block during surgery,” Medical & Biological Engineering & Computing, vol. 35, no. 5, pp. 498–503, 1997. View at Publisher · View at Google Scholar · View at Scopus
- J. A. Méndez, A. Marrero, J. A. Reboso, and A. León, “Adaptive fuzzy predictive controller for anesthesia delivery,” Control Engineering Practice, vol. 46, pp. 1–9, 2016. View at Publisher · View at Google Scholar · View at Scopus
- A. Abad-Gurumeta, J. Ripollés-Melchor, R. Casans-Francés, and J. Calvo-Vecino, “Monitorización de la nocicepción, realidad o ficción?” Revista Española de Anestesiología y Reanimación, vol. 64, no. 7, pp. 406–414, 2017. View at Publisher · View at Google Scholar
- M. Gruenewald and C. Ilies, “Monitoring the nociception-anti-nociception balance,” Best Practice & Research Clinical Anaesthesiology, vol. 27, no. 2, pp. 235–247, 2013. View at Publisher · View at Google Scholar · View at Scopus
- J.-S. Kang and M.-H. Lee, “Overview of therapeutic drug monitoring,” Korean Journal of Internal Medicine, vol. 24, no. 1, pp. 1–10, 2009. View at Publisher · View at Google Scholar · View at Scopus
- A. S. Gross, “Best practice in therapeutic drug monitoring,” British Journal of Clinical Pharmacology, vol. 52, pp. 5–9. View at Publisher · View at Google Scholar
- W. V. Tamborlane et al., “Continuous glucose monitoring and intensive treatment of type 1 diabetes,” The New England Journal of Medicine, vol. 359, no. 14, pp. 1464–1476, 2008. View at Publisher · View at Google Scholar
- B. W. Bode, H. Sabbah, and P. C. Davidson, “What's ahead in glucose monitoring? New techniques hold promise for improved ease and accuracy,” Postgraduate Medical Journal, vol. 109, no. 4, pp. 41–49, 2001. View at Publisher · View at Google Scholar · View at Scopus
- T. Musialowicz and P. Lahtinen, “Current status of EEG-based depth-of-consciousness monitoring during general anesthesia,” Current Anesthesiology Reports, vol. 4, no. 3, pp. 251–260, 2014. View at Publisher · View at Google Scholar
- M. Hallworth, “Therapeutic drug monitoring,” in Clarke's Analysis and Poisons, p. 59, 2011. View at Google Scholar
- M. C. Milone, “Analytical techniques used in therapeutic drug monitoring,” Therapeutic Drug Monitoring, pp. 49–73, 2012. View at Publisher · View at Google Scholar · View at Scopus
- K. Kuusniemi and R. Pöyhiä, “Present-day challenges and future solutions in postoperative pain management: results from PainForum 2014,” Journal of Pain Research, vol. 9, pp. 25–36, 2016. View at Publisher · View at Google Scholar · View at Scopus
- M. M. R. F. Struys, C. Vanpeteghem, M. Huiku, K. Uutela, N. B. K. Blyaert, and E. P. Mortier, “Changes in a surgical stress index in response to standardized pain stimuli during propofol-remifentanil infusion,” British Journal of Anaesthesia, vol. 99, no. 3, pp. 359–367, 2007. View at Publisher · View at Google Scholar · View at Scopus
- F. Von Dincklage, H. Velten, B. Rehberg, and J. H. Baars, “Monitoring of the responsiveness to noxious stimuli during sevoflurane mono-anaesthesia by using RIII reflex threshold and bispectral index,” British Journal of Anaesthesia, vol. 104, no. 6, pp. 740–745, 2010. View at Publisher · View at Google Scholar · View at Scopus
- R. K. Ellerkmann, A. Grass, A. Hoeft, and M. Soehle, “The response of the Composite Variability Index to a standardized noxious stimulus during propofol-remifentanil anesthesia,” Anesthesia & Analgesia, vol. 116, no. 3, pp. 580–588, 2013. View at Publisher · View at Google Scholar · View at Scopus
- B. Hullett, N. Chambers, J. Preuss et al., “Monitoring electrical skin conductance: a tool for the assessment of postoperative pain in children?” Anesthesiology, vol. 111, no. 3, pp. 513–517, 2009. View at Publisher · View at Google Scholar · View at Scopus
- R. Cowen, M. K. Stasiowska, H. Laycock, and C. Bantel, “Assessing pain objectively: The use of physiological markers,” Anaesthesia, vol. 70, no. 7, pp. 828–847, 2015. View at Publisher · View at Google Scholar · View at Scopus
- R. Logier, M. Jeanne, J. De Jonckheere, A. Dassonneville, M. Delecroix, and B. Tavernier, “PhysioDoloris: a monitoring device for analgesia/nociception balance evaluation using heart rate variability analysis,” in Proceedings of the 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC '10, pp. 1194–1197, September 2010. View at Publisher · View at Google Scholar · View at Scopus
- A. Gritsan, N. Dovbish, D. Kurnosov, and E. Gritsan, “Control of the adequacy of analgesia during general anesthesia with the use of the monitor analgesia nociception index,” Anesthesia & Analgesia, vol. 123, 2016. View at Google Scholar
- E. Boselli, M. Daniela-Ionescu, G. Bégou et al., “Prospective observational study of the non-invasive assessment of immediate postoperative pain using the analgesia/nociception index (ANI),” British Journal of Anaesthesia, vol. 111, no. 3, pp. 453–459, 2013. View at Publisher · View at Google Scholar · View at Scopus
- M. Gruenewald, T. Schoenherr, J. Herz, C. Ilies, A. Fudickar, and B. Bein, “Analgesia Nociception Index (ANI) for detection of noxious stimulation during sevofluran-remifentanil anaesthesia,” European Journal of Anaesthesiology, vol. 30, pp. 223–223, 2013. View at Publisher · View at Google Scholar
- L. Ursulet, J. Cros, J. De Jonckheere, P. Senges, A. Vincelot, and N. Nathan, “Bedside analysis of heart rate variability by Analgesia Nociception Index (ANI) predicts hypotension after spinal anesthesia for elective Caesarean delivery,” European Journal of Anaesthesiology, vol. 29, p. 5, 2012. View at Publisher · View at Google Scholar
- M. Jeanne, C. Clément, J. De Jonckheere, R. Logier, and B. Tavernier, “Variations of the analgesia nociception index during general anaesthesia for laparoscopic abdominal surgery,” Journal of Clinical Monitoring and Computing, vol. 26, no. 4, pp. 289–294, 2012. View at Publisher · View at Google Scholar · View at Scopus
- M. Sesay, A. Mainchain, M. Biais, D. Liguoro, and K. Nouette-Gaulain, “Impact of the Anesthesia Nociception Index on remifentanil consumption during anterior cervical discectomy,” Anesthesia and Analgesia, vol. 123, p. 428, 2016. View at Publisher · View at Google Scholar
- G. Daccache, S. Goursaud, E. Lemasson, L. Berger, J. Fellahi, and J. Hanouz, “Target-controlled dosing of remifentanil guided by the analgesia nociception index: a feasibility study,” European Journal of Anaesthesiology, vol. 31, p. 35, 2014. View at Publisher · View at Google Scholar
- M. Gruenewald, C. Ilies, J. Herz et al., “Influence of nociceptive stimulation on analgesia nociception index (ANI) during propofol-remifentanil anaesthesia,” British Journal of Anaesthesia, vol. 110, no. 6, pp. 1024–1030, 2013. View at Publisher · View at Google Scholar · View at Scopus
- G. Jess, E. M. Pogatzki-Zahn, P. K. Zahn, and C. H. Meyer-Frieem, “Monitoring heart rate variability to assess experimentally induced pain using the analgesia nociception index,” European Journal of Anaesthesiology, vol. 33, no. 2, pp. 118–125, 2016. View at Publisher · View at Google Scholar · View at Scopus
- Q. Yan, H. Y. An, and Y. Feng, “Pain assessment in conscious healthy volunteers: a crossover study evaluating the analgesia/nociception index,” British Journal of Anaesthesia, vol. 118, no. 4, pp. 635-636, 2017. View at Publisher · View at Google Scholar · View at Scopus
- P. S. Myles and N. Urquhart, “The linearity of the visual analogue scale in patients with severe acute pain,” Anaesthesia and Intensive Care Journal, vol. 33, no. 1, pp. 54–58, 2005. View at Google Scholar
- C. A. Bodian, G. Freedman, S. Hossain, J. B. Eisenkraft, and Y. Beilin, “The visual analog scale for pain: clinical significance in postoperative patients,” Anesthesiology, vol. 95, no. 6, pp. 1356–1361, 2001. View at Publisher · View at Google Scholar · View at Scopus
- J. A. Szental, A. Webb, C. Weeraratne, A. Campbell, H. Sivakumar, and S. Leong, “Postoperative pain after laparoscopic cholecystectomy is not reduced by intraoperative analgesia guided by analgesia nociception index (ANI®) monitoring: a randomized clinical trial,” British Journal of Anaesthesia, vol. 114, no. 4, pp. 640–645, 2015. View at Publisher · View at Google Scholar · View at Scopus
- A. Castro, F. G. de Almeida, P. Amorim, and C. S. Nunes, “A novel multivariate STeady-state index during general ANesthesia (STAN),” Journal of Clinical Monitoring and Computing, vol. 31, no. 4, pp. 851–860, 2017. View at Publisher · View at Google Scholar · View at Scopus
- M. Jeanne, M. Delecroix, J. De Jonckheere, A. Keribedj, R. Logier, and B. Tavernier, “Variations of the analgesia nociception index during propofol anesthesia for total knee replacement,” The Clinical Journal of Pain, vol. 30, no. 12, pp. 1084–1088, 2014. View at Publisher · View at Google Scholar · View at Scopus
- E. Boselli, R. Logier, L. Bouvet, and B. Allaouchiche, “Prediction of hemodynamic reactivity using dynamic variations of Analgesia/Nociception Index (Delta ANI),” Journal of Clinical Monitoring and Computing, vol. 30, no. 6, pp. 977–984, 2016. View at Publisher · View at Google Scholar · View at Scopus
- V. Podgorelec, P. Kokol, B. Stiglic, and I. Rozman, “Decision trees: an overview and their use in medicine,” Journal of Medical Systems, vol. 26, no. 5, pp. 445–463, 2002. View at Publisher · View at Google Scholar · View at Scopus
- T. G. Dietterich, “Experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization,” Machine Learning, vol. 40, no. 2, pp. 139–157, 2000. View at Publisher · View at Google Scholar · View at Scopus
- A. Colin, “Building decision trees with the ID3 algorithm,” Dr. Dobb's Journal, vol. 21, no. 6, pp. 107–109, 1996. View at Google Scholar · View at Scopus
- L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, vol. 19, 1984.
- A. Venkatasubramaniam, J. Wolfson, N. Mitchell, T. Barnes, M. Jaka, and S. French, “Decision trees in epidemiological research,” Emerging Themes in Epidemiology, vol. 14, no. 1, article 11, 2017. View at Publisher · View at Google Scholar · View at Scopus
- X. Wu and V. Kumar, “The Top Ten Algorithms in Data Mining,” 2009.
- R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence, vol. 2, pp. 1137–1143, 1995.
- P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison-Wesley Longman Publishing Co., Inc., Massachusetts, Mass, USA, 5th edition, 2005.
- H. Kehlet and K. Holte, “Effect of postoperative analgesia on surgical outcome,” British Journal of Anaesthesia, vol. 87, no. 1, pp. 62–72, 2001. View at Publisher · View at Google Scholar · View at Scopus
- H. Kehlet, T. S. Jensen, and C. J. Woolf, “Persistent postsurgical pain: risk factors and prevention,” The Lancet, vol. 367, no. 9522, pp. 1618–1625, 2006. View at Publisher · View at Google Scholar · View at Scopus
- P. Gueth, D. Dauvergne, N. Freud et al., “Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy,” Physics in Medicine and Biology, vol. 58, no. 13, pp. 4563–4577, 2013. View at Publisher · View at Google Scholar · View at Scopus
- C. Huang, R. Mezencev, J. F. McDonald, F. Vannberg, and B. Liu, “Open source machine-learning algorithms for the prediction of optimal cancer drug therapies,” PLoS ONE, vol. 12, no. 10, 2017. View at Publisher · View at Google Scholar
- M. Gram, J. Erlenwein, F. Petzke et al., “Prediction of postoperative opioid analgesia using clinical-experimental parameters and electroencephalography,” European Journal of Pain, vol. 21, no. 2, pp. 264–277, 2017. View at Publisher · View at Google Scholar · View at Scopus
- H. F. Galley, “II. Solid as a ROC,” British Journal of Anaesthesia, vol. 93, no. 5, pp. 623–626, 2004. View at Publisher · View at Google Scholar · View at Scopus
- N. R. Pal and S. Chakraborty, “Fuzzy rule extraction from ID3-type decision trees for real data,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 31, no. 5, pp. 745–754, 2001. View at Publisher · View at Google Scholar · View at Scopus
- K. Nozaki, H. Ishibuchi, and H. Tanaka, “Adaptive fuzzy rule-based classification systems,” IEEE Transactions on Fuzzy Systems, vol. 4, no. 3, pp. 238–250, 1996. View at Publisher · View at Google Scholar · View at Scopus
- J. S. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665–685, 1993. View at Publisher · View at Google Scholar · View at Scopus
- C. Olaru and L. Wehenkel, “A complete fuzzy decision tree technique,” Fuzzy Sets and Systems, vol. 138, no. 2, pp. 221–254, 2003. View at Publisher · View at Google Scholar · View at Scopus
- X. Wang, X. Liu, W. Pedrycz, and L. Zhang, “Fuzzy rule based decision trees,” Pattern Recognition, vol. 48, no. 1, pp. 50–59, 2015. View at Publisher · View at Google Scholar · View at Scopus
- C. Bockstaller, S. Beauchet, V. Manneville, B. Amiaud, and R. Botreau, “A tool to design fuzzy decision trees for sustainability assessment,” Environmental Modeling and Software, vol. 97, pp. 130–144, 2017. View at Publisher · View at Google Scholar · View at Scopus