Decision modeling study reveals a credibility-weighted linguistic evaluation framework for emergency COVID-19 triage, indicating improved accuracy in high-uncertainty clinical decision-making.
Probabilistic linguistic term sets (PLTSs) have many applications in the field of group decision making (GDM) because it includes both linguistic evaluation and probabilistic distribution when expressing preference information. However, the difference of information credibility in PLTSs is ignored, resulting in an inaccurate representation of decision information and unreasonable probability calculation. In this paper, we first consider the credibility of the information and propose the concept of Z-uncertain probabilistic linguistic variables (Z-UPLVs). Subsequently, the operational rules, normalization, distance and similarity measures, and comparison method of Z-UPLVs are introduced. Then, a probability calculation method based on credibility, an extended TOPSIS method, and some operators are proposed, which can be applied to emergency decision making in the Z-uncertain probabilistic linguistic (Z-UPL) environment. Finally, an emergency decision-making case of COVID-19 patients and comparative analysis illustrate the necessity and effectiveness of this method.
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Chai et al. (2020) studied this question.
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