Doctor Recommendation Based on an Intuitionistic Normal Cloud Model Considering Patient Preferences


Chinese medical websites help patients search for satisfactory doctors via the Internet regardless of time and location. Existing website systems recommend the same doctors for all patients using a global ranking but disregard patient preferences and online reviews. Additionally, these models do not consider the effects of interdependencies among criteria when making recommendations. We propose a systematic decision support model to improve such recommendations using intuitionistic fuzzy sets (IFSs) with the Bonferroni mean (BM) to address interdependencies. Our system accommodates patient preferences using multiple intuitionistic normal clouds (INCs). A case study using production data from, the largest such website, shows that our model improves the diversity and coverage of doctor recommendations while considering patient preferences when compared to the existing approach. This pattern continued with testing using data from several other Chinese healthcare sites. Our proposal is thus both applicable and readily implemented to improve the recommendations of these websites.

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This work was supported by the National Natural Science Foundation of China (Grant numbers 71871229, 71771219) and the Fundamental Research Funds for the Central Universities of Central South University (2018zzts092).

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Correspondence to Junhua Hu.

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Yang, Y., Hu, J., Liu, Y. et al. Doctor Recommendation Based on an Intuitionistic Normal Cloud Model Considering Patient Preferences. Cogn Comput 12, 460–478 (2020).

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  • Decision support model
  • Intuitionistic normal cloud model
  • Medical websites
  • Doctor recommendation