IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p1241-d1030248.html
   My bibliography  Save this article

A Textual Data-Oriented Method for Doctor Selection in Online Health Communities

Author

Listed:
  • Yinfeng Du

    (School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China)

  • Zhen-Song Chen

    (School of Civil Engineering, Wuhan University, Wuhan 430072, China)

  • Jie Yang

    (School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China)

  • Juan Antonio Morente-Molinera

    (Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071 Granada, Spain)

  • Lu Zhang

    (School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China)

  • Enrique Herrera-Viedma

    (Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071 Granada, Spain)

Abstract

As doctor–patient interactive platforms, online health communities (OHCs) offer patients massive information including doctor basic information and online patient reviews. However, how to develop a systematic framework for doctor selection in OHCs according to doctor basic information and online patient reviews is a challenged issue, which will be explored in this study. For doctor basic information, we define the quantification method and aggregate them to characterize relative influence of doctors. For online patient reviews, data analysis techniques (i.e., topics extraction and sentiment analysis) are used to mine the core attributes and evaluations. Subsequently, frequency weights and position weights are respectively determined by a frequency-oriented formula and a position score-based formula, which are integrated to obtain the final importance of attributes. Probabilistic linguistic-prospect theory-multiplicative multiobjective optimization by ratio analysis (PL-PT-MULTIMOORA) is proposed to analyze patient satisfactions on doctors. Finally, selection rules are made according to doctor influence and patient satisfactions so as to choose optimal and suboptimal doctors for rational or emotional patients. The designed textual data-driven method is successfully applied to analyze doctors from Haodf.com and some suggestions are given to help patients pick out optimal and suboptimal doctors.

Suggested Citation

  • Yinfeng Du & Zhen-Song Chen & Jie Yang & Juan Antonio Morente-Molinera & Lu Zhang & Enrique Herrera-Viedma, 2023. "A Textual Data-Oriented Method for Doctor Selection in Online Health Communities," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1241-:d:1030248
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/1241/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/2/1241/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Park, Jaehun & Lee, Byung Kwon, 2021. "An opinion-driven decision-support framework for benchmarking hotel service," Omega, Elsevier, vol. 103(C).
    2. Decui Liang & Zhuoyin Dai & Mingwei Wang & Jinjun Li, 2020. "Web celebrity shop assessment and improvement based on online review with probabilistic linguistic term sets by using sentiment analysis and fuzzy cognitive map," Fuzzy Optimization and Decision Making, Springer, vol. 19(4), pages 561-586, December.
    3. Liao, Huchang & Wu, Xingli, 2020. "DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making," Omega, Elsevier, vol. 94(C).
    4. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    5. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    6. Yinfeng Du & Dun Liu, 2021. "A novel approach to relative importance ratings of customer requirements in QFD based on probabilistic linguistic preferences," Fuzzy Optimization and Decision Making, Springer, vol. 20(3), pages 365-395, September.
    7. Yang, Wenjuan & Zhang, Jiantong & Yan, Hong, 2021. "Impacts of online consumer reviews on a dual-channel supply chain," Omega, Elsevier, vol. 101(C).
    8. Yinfeng Du & Dun Liu & Hengxin Duan, 2022. "A textual data-driven method to identify and prioritise user preferences based on regret/rejoicing perception for smart and connected products," International Journal of Production Research, Taylor & Francis Journals, vol. 60(13), pages 4176-4196, July.
    9. Wu, Xingli & Liao, Huchang, 2021. "Modeling personalized cognition of customers in online shopping," Omega, Elsevier, vol. 104(C).
    10. Mohammed Abdellaoui, 2000. "Parameter-Free Elicitation of Utility and Probability Weighting Functions," Management Science, INFORMS, vol. 46(11), pages 1497-1512, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Fan & Liao, Huchang & Al-Barakati, Abdullah, 2023. "Physician selection based on user-generated content considering interactive criteria and risk preferences of patients," Omega, Elsevier, vol. 115(C).
    2. Mohammed Abdellaoui & Olivier L’Haridon & Horst Zank, 2010. "Separating curvature and elevation: A parametric probability weighting function," Journal of Risk and Uncertainty, Springer, vol. 41(1), pages 39-65, August.
    3. Hamza Bahaji, 2011. "Incentives from stock option grants: a behavioral approach," Post-Print halshs-00681607, HAL.
    4. Ariane Charpin, 2018. "Tests des modèles de décision en situation de risque. Le cas des parieurs hippiques en France," Revue économique, Presses de Sciences-Po, vol. 69(5), pages 779-803.
    5. Luís Santos-Pinto & Adrian Bruhin & José Mata & Thomas Åstebro, 2015. "Detecting heterogeneous risk attitudes with mixed gambles," Theory and Decision, Springer, vol. 79(4), pages 573-600, December.
    6. Dichtl, Hubert & Drobetz, Wolfgang, 2011. "Portfolio insurance and prospect theory investors: Popularity and optimal design of capital protected financial products," Journal of Banking & Finance, Elsevier, vol. 35(7), pages 1683-1697, July.
    7. Jakusch, Sven Thorsten, 2017. "On the applicability of maximum likelihood methods: From experimental to financial data," SAFE Working Paper Series 148, Leibniz Institute for Financial Research SAFE, revised 2017.
    8. Thomas Epper & Helga Fehr-Duda & Adrian Bruhin, 2011. "Viewing the future through a warped lens: Why uncertainty generates hyperbolic discounting," Journal of Risk and Uncertainty, Springer, vol. 43(3), pages 169-203, December.
    9. LiCalzi, Marco & Sorato, Annamaria, 2006. "The Pearson system of utility functions," European Journal of Operational Research, Elsevier, vol. 172(2), pages 560-573, July.
    10. Ulrich Schmidt & Horst Zank, 2008. "Risk Aversion in Cumulative Prospect Theory," Management Science, INFORMS, vol. 54(1), pages 208-216, January.
    11. Loreto Llorente & Josemari Aizpurua, 2008. "A Betting Market: Description and a Theoretical Explanation of Bets in Pelota Matches," Theory and Decision, Springer, vol. 64(2), pages 421-446, March.
    12. Schunk, Daniel, 2009. "Behavioral heterogeneity in dynamic search situations: Theory and experimental evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 33(9), pages 1719-1738, September.
    13. Julius Pahlke & Sebastian Strasser & Ferdinand Vieider, 2015. "Responsibility effects in decision making under risk," Journal of Risk and Uncertainty, Springer, vol. 51(2), pages 125-146, October.
    14. Sanjit Dhami & Narges Hajimoladarvish, 2020. "Mental Accounting, Loss Aversion, and Tax Evasion: Theory and Evidence," CESifo Working Paper Series 8606, CESifo.
    15. Ulrich Schmidt & Horst Zank, 2012. "A genuine foundation for prospect theory," Journal of Risk and Uncertainty, Springer, vol. 45(2), pages 97-113, October.
    16. Gul, Faruk & Pesendorfer, Wolfgang, 2015. "Hurwicz expected utility and subjective sources," Journal of Economic Theory, Elsevier, vol. 159(PA), pages 465-488.
    17. Han Bleichrodt, 2002. "A new explanation for the difference between time trade‐off utilities and standard gamble utilities," Health Economics, John Wiley & Sons, Ltd., vol. 11(5), pages 447-456, July.
    18. Abdellaoui, Mohammed & Bleichrodt, Han, 2007. "Eliciting Gul's theory of disappointment aversion by the tradeoff method," Journal of Economic Psychology, Elsevier, vol. 28(6), pages 631-645, December.
    19. Joost M. E. Pennings & Ale Smidts, 2003. "The Shape of Utility Functions and Organizational Behavior," Management Science, INFORMS, vol. 49(9), pages 1251-1263, September.
    20. Han Bleichrodt & Jose Maria Abellan-Perpiñan & Jose Luis Pinto-Prades & Ildefonso Mendez-Martinez, 2007. "Resolving Inconsistencies in Utility Measurement Under Risk: Tests of Generalizations of Expected Utility," Management Science, INFORMS, vol. 53(3), pages 469-482, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1241-:d:1030248. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.