The optimal treatment alternatives selection of integrated TCM and Western medicine based on dynamic conflict group decision-making models

Abstract

Group conflict decision-making is a crucial research direction in group decision making. Most of the existing group conflict decision theories provide models under the condition of static and complete information, but it is urgent to improve the dynamic conflict group decision-making theory with incomplete information to deal with practical management problems. This paper presents a group optimal alternative selection model with dynamic conflict characteristics under incomplete information. Firstly, a directed graph is used to describe the dynamic decision process with incomplete information, and the directed graph is transformed into a generalized conflict information system with incomplete information. Secondly, a group dynamic conflict decision-making model based on the conflict information system is built. Thirdly, dynamic decision rules for decision makers on alternatives under decision attributes are designed, then the largest coalition and the path dependence measurement of decision makers are calculated. Subsequently, taking the optimization decision-making processes of clinical treatment alternatives of integrated traditional Chinese medicine (TCM) and Western medicine as the research problem, the proposed model is applied to verify the applicability and effectiveness by using the electronic medical record (EMR) of 2481 samples. Finally, the solution of the set of clinical optimal treatment alternatives and the path dependence of decision makers on the set of optimal treatment alternatives are presented. The conclusion shows that the distribution of missing information is generally consistent with the practice of clinical decision-making. In addition, the path dependence of decision makers on the optimal alternative is much higher than that on the worst alternative, the conflicts among decision makers are consistent, and decision makers gradually approach the optimal alternative through repeated comparison and verification.