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Volume 2018, Article ID 9012720, 15 pages
Research Article

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.


One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index (ANI) as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery.