计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (10): 28-36.doi: 10.11896/jsjkx.230600042

• 粒计算与知识发现 • 上一篇    下一篇

考虑需求优先性的在线医患双边匹配方法

范婷睿1, 刘盾1, 叶晓庆2   

  1. 1 西南交通大学经济管理学院 成都610031
    2 西南交通大学计算机与人工智能学院 成都611756
  • 收稿日期:2023-06-05 修回日期:2023-07-28 出版日期:2023-10-10 发布日期:2023-10-10
  • 通讯作者: 刘盾(newton83@163.com)
  • 作者简介:(fantingrui128@163.com)
  • 基金资助:
    国家自然科学基金(62276217,61876157);四川省杰出青年科学基金(2022JDJQ0034);中国博士后科学基金面上资助(2022M722629);中央高校基本科研业务费资助(2682022ZTPY057)

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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摘要： 近年来,随着互联网与智慧医疗的飞速发展,在线问诊平台逐渐成为满足大众基本医疗需求的重要渠道。随着在线问诊平台患者和医生数量的不断增长,医生回答参差不齐、患者提问响应不及时、答复率严重不足等问题不断涌现。如何从大量的在线医疗内容中挖掘患者需求信息和医生服务信息,刻画患者需求满意度和医生的服务能力,实现精确匹配是亟需解决的问题。基于此,文中提出了一个融合机器学习算法的多阶段匹配模型,用于提高匹配精度和多样性。首先,从医生和患者两个视角,利用机器学习算法和情感分析工具,深度挖掘患者服务需求和医生服务能力,同时考虑到患者强烈的风险规避态度,引入前景理论描述患者风险偏好。然后,考虑到患者需求的优先性,以粒计算思想为指导,以提高医患匹配的准确性和多样性为目的, 构建多阶段动态匹配模型。最后,通过爬取好大夫网站的真实数据来进行实验,结果验证了所提方法的有效性。

关键词: 线问诊平台, 医患双边匹配, 粒计算, 用户生成内容, 多源信息

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

中图分类号: 

  • TP181
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