Abstract
Currently, patients tend to browse online reviews before choosing a satisfactory doctor. To better help patients make such choices, a doctor recommendation model under a probabilistic linguistic environment with an unbalanced semantic distribution is developed. First, a probabilistic linguistic term set (PLTS) is utilized to represent online evaluation information on doctors via natural language processing tools due to its advantage in modeling uncertainty in the online linguistic context. Then, to depict the different impacts of positive and negative evaluations in the context of doctor recommendations, the unbalanced semantic functions of a PLTS are proposed. Moreover, a comprehensive weight-determining method that combines the term frequency-inverse document frequency algorithm and the Word2Vec algorithm is developed considering the interrelationship among different criteria on patients’ decisions. An extended technique for order performance by similarity to the ideal solution method based on PLTS is proposed. Finally, the proposed method is verified in a case study on haodf.com. The findings show that it is vital to consider patients’ different risk preferences when building an online doctor recommendation model.
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This work was supported by the Major Research Program Integration Project of the National Natural Science Foundation of China (No. 91846301) and the National Natural Science Foundation of China (No. 71971223).
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Chen, X., Wang, H. & Li, X. Doctor recommendation under probabilistic linguistic environment considering patient’s risk preference. Ann Oper Res (2022). https://doi.org/10.1007/s10479-022-04843-9
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DOI: https://doi.org/10.1007/s10479-022-04843-9