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

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Abstract

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 haodf.com, 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 haodf.com 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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Funding

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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  1. School of Business, Central South University, Changsha, 410083, China

    Yan Yang, Junhua Hu, Yongmei Liu & Xiaohong Chen

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  1. Yan Yang
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  2. Junhua Hu
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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). https://doi.org/10.1007/s12559-018-9616-3

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