Decision support for personalized hospital choice using the DEX hierarchical model with SMAA

Despite an ever-growing personalized demand for patients’ hospital choice, little systematic work has examined the decision process that considers the diversity of medical service demand. In this paper, we develop an intelligence decision framework to explore multi-source uncertain information in hospital choice. The framework employs a novel SMAA-DEX method to generate a ranking list of hospital alternatives based on Decision EXpert (DEX) hierarchical model and stochastic multicriteria acceptability analysis (SMAA) in personalized hospital choice. To conduct the multi-source information fusion under uncertainty, the SMAA-DEX method produces the central weight vector considering the ordinal weight and random weight and estimates the holistic acceptability indices for each alternative. By collecting hospital statistics, third-party evaluations and personal patient information in the real world, we verify our method for personalized hospital choice in terms of different preferences such as distance, ranking and income. The results of the experiments demonstrate the effectiveness of the proposed approach, which not only effectively processes various types of hospital choice, but also accomplishes uncertain reasoning of multi-source online information.