Fuzzy linguistic preference relations approach: Evaluation in quality of healthcare
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 0302-9743, Vol: 8211 LNAI, Page: 316-323
2013
- 3Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Conference Paper Description
This study proposes a linguistic preference relations approach to evaluate the quality of healthcare under a fuzzy environment. Pairwise comparisons are utilized to derive the importance weights of evaluation criteria and to obtain the performance rating of feasible healthcare organizations. The subjectivity and vagueness in the evaluation processes are dealt with linguistic variables parameterized by triangular fuzzy numbers. By calculating the distance of each feasible healthcare organization to the fuzzy positive ideal reference point (FPIRP) and the fuzzy negative ideal reference point (FNIRP) respectively, a closeness coefficient is obtained and utilized to rank the order of all feasible healthcare organizations. A case is simultaneously shown to demonstrate the computational procedures of this proposed approach. © Springer International Publishing 2013.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84892911785&origin=inward; http://dx.doi.org/10.1007/978-3-319-02753-1_32; http://link.springer.com/10.1007/978-3-319-02753-1_32; http://link.springer.com/content/pdf/10.1007/978-3-319-02753-1_32; https://dx.doi.org/10.1007/978-3-319-02753-1_32; https://link.springer.com/chapter/10.1007/978-3-319-02753-1_32
Springer Science and Business Media LLC
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