Evaluation of oral antiviral treatments for chronic Hepatitis B using fuzzy PROMETHEE

Sarıgül, Figen; Hülagü, Sadettin; Ozsahin, Dilber Uzun

doi:10.1016/b978-0-12-824086-1.00017-7

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…Multi-attribute methods are widely used in medical environments, providing trusted environments where decisions can be made once the criteria have been established. Thus, the analytical hierarchical process (AHP) [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ], TOPSIS [ 17 , 18 , 19 ], MAUT [ 10 , 16 , 20 , 21 ], PROMETHEE [ 22 , 23 , 24 , 25 ] and VIKOR [ 26 , 27 , 28 ] methods, among others, are applied to decision management in medical–sanitary environments. However, the increasing need for incorporating and controlling inaccuracy in information and the judgments in decision making have popularized the use of methods that are capable of controlling such decision uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Design and Definition of a New Decision Support System Aimed to the Hierarchization of Patients Candidate to Be Admitted to Intensive Care Units

et al. 2022

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The triage processes prior to the assignation of healthcare resources in hospitals are some of the decision-making processes that more severely affect patients. This effect gets even worse in health emergency situations and intensive care units (ICUs). Aiming to facilitate the decision-making process, in this work the use of vague fuzzy numbers is proposed, aiming to define a multi-attribute patient hierarchization method to be used in emergency situations at hospital ICUs. The incorporation of fuzzy models allows for modelling the vagueness and uncertainty associated with decision criteria evaluation, with which more efficient support is provided to the decision-making process. After defining the methodology, the effectiveness of this new system for patient hierarchization is shown in a case study. As a consequence of that, it is proved that the integration of decision-support systems into healthcare environments results to be efficient and productive, suggesting that if a part of the decision process is supported by these systems, then the errors associated with wrong interpretations and/or diagnoses might be reduced.

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