A 2-additive Choquet integral-based multi-criterion decision-making method with complex linguistic information in drug value assessment

Abstract

With the escalating incidence and mortality rates of lung cancer, the targeted drugs assessment has emerged a key problem, serving as the foundation for incorporating targeted drugs into the national basic medical insurance. This paper executes drug value assessment within a multi-criterion decision-making framework. Recognizing that experts naturally provide assessments in linguistic representations during drug evaluations, we employ the generalised probabilistic linguistic term set to uniformly model diverse types of linguistic information. Considering the bounded rationality of experts, we propose a method to obtain expert weights through minimizing regret degree of experts. Based on the determined weights of experts, a method to simplify the generalised probabilistic linguistic term set is developed. Afterwards, this study establishes a 2-additional Choquet integral-based aggregation method to fuse the assessments of drugs considering interactive criteria. To examine the effectiveness of the proposed method, we apply the proposed method in drug value assessment for lung cancer. The proposed method provides a feasible attempt to address decision-making problems involving complex linguistic information.