Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (10): 28-36.doi: 10.11896/jsjkx.230600042

• Granular Computing & Knowledge Discovery • Previous Articles     Next Articles

Two-sided Matching Method for Online Consultation Platform Considering Demand Priority

FAN Tingrui1, LIU Dun1, YE Xiaoqing2   

  1. 1 School of Economics and Management,Southwest Jiaotong University,Chengdu 610031,China
    2 School of Computer and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China
  • Received:2023-06-05 Revised:2023-07-28 Online:2023-10-10 Published:2023-10-10
  • About author:FAN Tingrui,born in 1999,postgra-duate.Her main research interests include data mining,medical operations management,three-way decision and granular computing.LIU Dun,born in 1983,Ph.D,professor.His main research interests include data mining and knowledge discovery,rough set theory and granular computing,decision support systems.
  • Supported by:
    National Natural Science Foundation of China(62276217,61876157),Science Fund for Distinguished Young Scholars of Sichuan Province(2022JDJQ0034),China Postdoctoral Science Foundation(2022M722629) and Fundamental Research Funds for the Central Universities of Ministry of Education of China(2682022ZTPY057).

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Abstract: In recent years,with the rapid development of the Internet and smart medical care,online consultation platforms have gradually become an important channel to meet the basic medical needs of the public.With the continuous increase in the number of patients and doctors on the online consultation platform,the quality of doctors' consultation responses is uneven,and problems such as untimely response to patients' questions and a serious shortage of response rates continue to emerge.Therefore,How to mine patients' demand information and doctors' service information from a large amount of online medical content,describe patients' demand satisfaction and doctors' service ability,and achieve accurate matching are problems that need to be solved.Based on this,this paper proposes a multi-stage matching model combined with machine learning algorithms to improve matching accuracy and diversity.First of all,from the perspective of doctors and patients,this paper uses machine learning algorithms and sentiment analysis tools,combined with prospect theory,to fully evaluate patient preferences and doctors' professional capabilities.Secondly,considering the hierarchical structure of patient needs,this paper constructs a multi-stage dynamic matching model guided by the idea of granular computing.Finally,the validity of the method is verified through the research on real database on haodf.com.

Key words: Online consultation platform, Patients-Doctors matching, Granular computing, User-generated content, Multi-source data

CLC Number: 

  • TP181
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