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

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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

References

  1. 1.

    Li J, Wang JQ. Multi-criteria outranking methods with hesitant probabilistic fuzzy sets. Cogn Comput. 2017;9(5):611–25.

    Article  Google Scholar 

  2. 2.

    Li X, Chen X. D-intuitionistic hesitant fuzzy sets and their application in multiple attribute decision making. Cogn Comput. 2018;10(3):496–505.

    Article  Google Scholar 

  3. 3.

    Ji P, Zhang H, Wang JQ. A projection-based outranking method with multi-hesitant fuzzy linguistic term sets for hotel location selection. Cogn Comput. 2018;10:737–51. https://doi.org/10.1007/s12559-018-9552-2.

    Article  Google Scholar 

  4. 4.

    Farhadinia B. A multiple criteria decision making model with entropy weight in an interval-rransformed hesitant fuzzy environment. Cogn Comput. 2017;9(4):513–25.

    Article  Google Scholar 

  5. 5.

    Liu P, Li H. Interval-valued intuitionistic fuzzy power Bonferroni aggregation operators and their application to group decision making. Cogn Comput. 2017;9(4):492–512.

    Google Scholar 

  6. 6.

    Liu P, Zhang X. A novel picture fuzzy linguistic aggregation operator and its application to group decision-making. Cogn Comput. 2018;10(2):242–59.

    Article  Google Scholar 

  7. 7.

    Tao Z, Han B, Chen H. On intuitionistic fuzzy copula aggregation operators in multiple-attribute decision making. Cogn Comput. 2018;1:1–15. https://doi.org/10.1007/s12559-018-9545-1.

    Article  Google Scholar 

  8. 8.

    Resnick P, Varian HR. Recommender systems. Commun ACM. 1997;40(3):56–8.

    Article  Google Scholar 

  9. 9.

    Zhang Z, Zhao X, Wang G. FE-ELM: a new friend recommendation model with extreme learning machine. Cogn Comput. 2017;9(5):659–70.

    Article  Google Scholar 

  10. 10.

    Schuckert M, Liu X, Law R. Hospitality and tourism online reviews: recent trends and future directions. J Travel Tour Mark. 2015;32(5):608–21.

    Article  Google Scholar 

  11. 11.

    Liu X, Lu R, Ma J, Chen L, Qin B. Privacy-preserving patient-centric clinical decision support system on naïve bayesian classification. IEEE Journal of Biomedical & Health Informatics. 2016;20(2):655–68.

    Article  Google Scholar 

  12. 12.

    Wang MX, Wang JQ. New online recommendation approach based on unbalanced linguistic label with integrated cloud. Kybernetes. 2018;47(7):1325–47. https://doi.org/10.1108/K-06-2017-0211.

    Article  Google Scholar 

  13. 13.

    Wang JQ, Zhang X, Zhang HY. Hotel recommendation approach based on the online consumer reviews using interval neutrosophic linguistic numbers. J Intell Fuzzy Syst. 2018;34(1):381–94. https://doi.org/10.3233/JIFS-171421.

    CAS  Article  Google Scholar 

  14. 14.

    Guy I and Carmel D (2015). Social recommender systems. In: Xavier A, Josep M P (eds). Recommender Systems Handbook.pp 511–43.

    Google Scholar 

  15. 15.

    Li YM, Wu CT, Lai CY. A social recommender mechanism for e-commerce: combining similarity, trust, and relationship. Decis Support Syst. 2013;55(3):740–52.

    Article  Google Scholar 

  16. 16.

    Davoodi E, Kianmehr K, Afsharchi M. A semantic social network-based expert recommender system. Appl Intell. 2013;39(1):1–13.

    Article  Google Scholar 

  17. 17.

    Xiao W, Yao S and Wu S. Improving on recommend speed of recommender systems by using expert users. Control and Decision Conference (CCDC), 2016 Chinese 2016: 2425–30.

  18. 18.

    Atanassov KT. Intuitionistic fuzzy sets. Fuzzy Sets Syst. 1986;20(1):87–96. https://doi.org/10.1016/S0165-0114(86)80034-3.

    Article  Google Scholar 

  19. 19.

    Xu Z. Intuitionistic fuzzy aggregation operators. Ieee T Fuzzy Syst. 2007;15(6):1179–87.

    Article  Google Scholar 

  20. 20.

    Wei G. Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision making. Appl Soft Comput. 2010;10(2):423–31.

    Article  Google Scholar 

  21. 21.

    Atanassov K, Gargov G. Interval valued intuitionistic fuzzy sets. Fuzzy Sets Syst. 1989;31(3):343–9.

    Article  Google Scholar 

  22. 22.

    Yu SM, Wang J, Wang JQ. An extended TODIM approach with intuitionistic linguistic numbers. Int Trans Oper Res. 2018;25(3):781–805.

    Article  Google Scholar 

  23. 23.

    Rodríguez A, Ortega F, Concepción R. An intuitionistic method for the selection of a risk management approach to information technology projects. Inform Sci. 2017;375:202–18.

    Article  Google Scholar 

  24. 24.

    Hu JH, Zhang XH, Yang Y, Liu YM, Chen XH. New doctors ranking system based on VIKOR method. Int Trans Oper Res. 2018. https://doi.org/10.1111/itor.12569.

    Article  Google Scholar 

  25. 25.

    Hu JH, Yang Y, Zhang XL, Chen XH. Similarity and entropy measures for hesitant fuzzy sets. Int Trans Oper Res. 2018;25(3):857–86. https://doi.org/10.1111/itor.12477.

    Article  Google Scholar 

  26. 26.

    Yang Y, Hu JH, An QX, Chen XH. Group decision making with multiplicative triangular hesitant fuzzy preference relations and cooperative games method. Int J Uncertain Quantif. 2017;7(3):271–84. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2017020126.

    Article  Google Scholar 

  27. 27.

    Gao H, Wei G, Huang Y. Dual hesitant bipolar fuzzy Hamacher prioritized aggregation operators in multiple attribute decision making. IEEE Access. 2018;6(1):11508–22.

    Article  Google Scholar 

  28. 28.

    Tian ZP, Wang J, Wang JQ, Zhang HY. Simplified neutrosophic linguistic multi-criteria group decision-making approach to green product development. Group Decis Negotiation. 2017;26(3):597–627.

    Article  Google Scholar 

  29. 29.

    Wei G, Lu M. Pythagorean fuzzy power aggregation operators in multiple attribute decision making. Int J Intell Syst. 2018;33(1):169–86.

    Article  Google Scholar 

  30. 30.

    Wei G, Wei Y. Similarity measures of Pythagorean fuzzy sets based on the cosine function and their applications. Int J Intell Syst. 2018;33(3):634–52.

    Article  Google Scholar 

  31. 31.

    Wei GW, Gao H. The generalized dice similarity measures for picture fuzzy sets and their applications. Informatica. 2018;29(1):1–18.

    Article  Google Scholar 

  32. 32.

    Wei G. Picture uncertain linguistic Bonferroni mean operators and their application to multiple attribute decision making. Kybernetes. 2017;46(10):1777–800.

    Article  Google Scholar 

  33. 33.

    Wei GW. Some cosine similarity measures for picture fuzzy sets and their applications to strategic decision making. Informatica. 2017;28(3):547–64.

    Article  Google Scholar 

  34. 34.

    Li D, Meng H, Shi X. Membership clouds and membership cloud generators. J Comput Res Dev. 1995;6(32):15–20.

    Google Scholar 

  35. 35.

    Li D, Du Y. Artificial intelligence with uncertainty. International Conference on Computer and Information Technology. 2008;15(11):2.

  36. 36.

    Wang G, Xu C, Li D. Generic normal cloud model. Inform Sci. 2014;280:1–15.

    Article  Google Scholar 

  37. 37.

    Petri I, Li H, Rezgui Y, Chunfeng Y, Yuce B, Jayan B. A HPC based cloud model for real-time energy optimisation. Enterp Inf Syst. 2016;10(1):108–28.

    Article  Google Scholar 

  38. 38.

    Wang JQ, Yang WE. Multiple criteria group decision making method based on intuitionistic normal cloud by Monte Carlo simulation. Syst Eng Theory Pract. 2013;33(11):2859–65.

    Google Scholar 

  39. 39.

    Xu Z, Yager RR. Intuitionistic fuzzy Bonferroni means. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 2011;41(2):568–78.

    Article  Google Scholar 

  40. 40.

    Liu P, Liu J, Chen S-M. Some intuitionistic fuzzy Dombi Bonferroni mean operators and their application to multi-attribute group decision making. J Oper Res Soc. 2018;69(1):1–24.

    Article  Google Scholar 

  41. 41.

    Garg H, Arora R. Bonferroni mean aggregation operators under intuitionistic fuzzy soft set environment and their applications to decision-making. J Oper Res Soc. 2018:1–14.

  42. 42.

    Liu P, Chen S-M, Liu J. Multiple attribute group decision making based on intuitionistic fuzzy interaction partitioned Bonferroni mean operators. Inform Sci. 2017;411:98–121.

    Article  Google Scholar 

  43. 43.

    Kim HN, El-Saddik A, Jo GS. Collaborative error-reflected models for cold-start recommender systems. Decis Support Syst. 2011;51(3):519–31.

    Article  Google Scholar 

  44. 44.

    Huang TCK, Chen YL, Chen MC. A novel recommendation model with Google similarity. Decis Support Syst. 2016;89:17–27.

    Article  Google Scholar 

  45. 45.

    Negre E, Ravat F, Teste O, Tournier R. Cold-start recommender system problem within a multidimensional data warehouse. IEEE 7th International Conference on Research Challenges in Information Science (RCIS). 2013:1–8.

  46. 46.

    Li YM, Lin LF, Ho CC. A social route recommender mechanism for store shopping support. Decis Support Syst. 2016;94:97–108.

    CAS  Article  Google Scholar 

  47. 47.

    Zhang Y. GroRec: a group-centric intelligent recommender system integrating social, mobile and big data technologies. IEEE Trans Serv Comput. 2016;9(5):1.

    Article  Google Scholar 

  48. 48.

    Atanassov KT, Rangasamy P. Intuitionistic fuzzy sets. VII ITKR. 1983.

  49. 49.

    Atanassov KT, Rangasamy P. Intuitionistic fuzzy sets. Fuzzy Sets & Systems. 1986;20(1):87–96.

    Article  Google Scholar 

  50. 50.

    Le HS, Thong NT. Intuitionistic fuzzy recommender systems: an effective tool for medical diagnosis. Knowl-Based Syst. 2015;74:133–50. https://doi.org/10.1016/j.knosys.2014.11.012.

    Article  Google Scholar 

  51. 51.

    Thong NT, Le HS. HIFCF: an effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis. Expert Syst Appl. 2015;42(7):3682–701.

    Article  Google Scholar 

  52. 52.

    Guan C, Yuen K K F and Coenen F. Towards an intuitionistic fuzzy agglomerative hierarchical clustering algorithm for music recommendation in folksonomy. IEEE International Conference on Systems, Man, and Cybernetics 2016: 2039–42.

  53. 53.

    Wang G Y, Xu C L and Li D Y. Generic normal cloud model Inform Sciences 2014; 280: 1–15.

  54. 54.

    Wang D, Liu DF, Ding H, Singh VP, Wang YK, Zeng XK, et al. A cloud model-based approach for water quality assessment. Environ Res. 2016;148:24–35.

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Wang D, Zeng DB, Singh VP, Xu PC, Liu D, Wang YK, et al. A multidimension cloud model-based approach for water quality assessment. Environ Res. 2016;149:113–21.

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Zhang LM, Wu XG, Chen QQ, Skibniewski MJ, Zhong JB. Developing a cloud model based risk assessment methodology for tunnel-induced damage to existing pipelines. Stoch Env Res Risk A. 2015;29(2):513–26.

    Article  Google Scholar 

  57. 57.

    Zhang HY, Ji P, Wang JQ, Chen XH. A neutrosophic normal cloud and its application in decision-making. Cogn Comput. 2016;8(4):649–69.

    CAS  Article  Google Scholar 

  58. 58.

    Agarwal B, Poria S, Mittal N, Gelbukh A, Hussain A. Concept-level sentiment analysis with dependency-based semantic parsing: a novel approach. Cogn Comput. 2015;7(4):487–99.

    Article  Google Scholar 

  59. 59.

    Giatsoglou M, Vozalis MG, Diamantaras K, Vakali A, Sarigiannidis G, Chatzisavvas KC. Sentiment analysis leveraging emotions and word embeddings. Expert Syst Appl. 2017;69:214–24.

    Article  Google Scholar 

  60. 60.

    Zhu B, Xu ZS. Hesitant fuzzy Bonferroni means for multi-criteria decision making. J Oper Res Soc. 2013;64(12):1831–40.

    Article  Google Scholar 

  61. 61.

    Tian ZP, Wang J, Wang JQ, Chen XH. Multicriteria decision-making approach based on gray linguistic weighted Bonferroni mean operator. Int Trans Oper Res. 2018;25(5):1635–58. https://doi.org/10.1111/itor.12220.

    Article  Google Scholar 

  62. 62.

    Wang J-g, Peng J-j, Zhang H-y, Liu T, Chen X-h. An uncertain linguistic multi-criteria group decision-making method based on a cloud model. Group Decis Negot. 2015;24(1):171–92.

    Article  Google Scholar 

  63. 63.

    Zhou T, Kuscsik Z, Liu JG, Medo M, Wakeling JR, Zhang YC. Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences of the USA. PNAS. 2010;107(10):4511–5.

    CAS  PubMed  Article  Google Scholar 

  64. 64.

    Gogna A, Majumdar A. DiABlO: optimization based design for improving diversity in recommender system. Inform Sci. 2017;378:59–74.

    Article  Google Scholar 

  65. 65.

    Kunaver M, Požrl T. Diversity in recommender systems—a survey. Knowl-Based Syst. 2017;123:154–62.

    Article  Google Scholar 

  66. 66.

    Ricci F, Rokach L and Shapira B. Introduction to recommender systems handbook. Place: Springer; year.

  67. 67.

    Shani G and Gunawardana A (2011). Evaluating recommendation systems. In: (eds). Recommender systems handbook.pp 257–97.

    Google Scholar 

  68. 68.

    Pu P, Faltings B, Chen L, Zhang J and Viappiani P (2011). Usability guidelines for product recommenders based on example critiquing research. In: Xavier A, Josep M P (eds). Recommender Systems Handbook.pp 511–45.

    Google Scholar 

Download references

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).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Junhua Hu.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Informed Consent

Informed consent was not required as no human or animals were involved.

Human and Animal Rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

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