面向在线问诊平台的医生推荐方法及应用研究综述  

Review of Doctor Recommendation Methods and Applications for Consultation Platforms

作　　者：吴性丽 张皓月 廖虎昌 WU Xingli;ZHANG Haoyue;LIAO Huchang(Business School,Sichuan University,Chengdu 610064,China)

机构地区：[1]四川大学商学院,成都610064

出　　处：《计算机科学》2025年第5期109-121,共13页Computer Science

基　　金：国家自然科学基金(72301186);四川省科技计划项目(2024NSFSC1065)。

摘　　要：文章旨在强化个性化推荐技术在互联网医疗场景下的应用,辅助患者选择优质的医生资源,解决在线问诊规模不断扩大带来的信息过载问题。通过文献计量归纳热门研究方向,系统梳理现有在线医生推荐模式。根据医患匹配原理,将现有模式划分为5类:基于传统推荐算法的推荐模式、基于多属性决策的推荐模式、基于机器学习的推荐模式、混合推荐模式,以及其他推荐模式。对比各模式的应用现状、优缺点及适用范围,分析发展趋势并提出未来研究方向。在线医生推荐属于计算机科学、管理学及医学领域的交叉研究问题,相较于传统的推荐系统,它更侧重于对患者病情与医生专业领域的精准匹配。传统推荐算法在在线医生推荐领域应用较早,但受限于数据稀疏性与冷启动问题。基于多属性决策的推荐模式理论扎实,能灵活反映患者偏好,但对系统与患者间的交互需求高。基于机器学习的推荐模式能缓解数据稀疏难题,实现智能推荐,但需大量数据支持且欠缺可解释性。混合推荐模式通过整合多种算法优势,有望提升推荐效率与精准度,然而,如何有效组合与平衡各算法成为关键挑战。此外,基于优化理论与图模型等的推荐模式尚待深入研究。未来还需融合多学科理论方法,对跨平台多源异构型医患数据的挖掘、表达、整合进行研究,探索基于患者个性化需求及偏好的医生推荐模式。This paper aims to strengthen the use of personalized recommendation technologies in online medical settings,help patients choose resources for high-quality physicians,and address the information overload caused by the growing volume of online consultations.Firstly,bibliometrics summarizes popular research directions.On this basis,this paper sorts out the existing online doctor recommendation methods and classifies them into five categories based on doctor-patient matching:recommendation based on traditional recommendation algorithms,recommendation based on multi-attribute decision making,recommendation based on machine learning,hybrid recommendation,and others.In addition,we compare the application status,advantages and disadvantages,and the application scope of each category.Finally,we analyze the trend of online doctor recommendations and propose future research directions.Online doctor recommendation belongs to the intersection of research problems in the fields of computer science,management,and medicine.In contrast to traditional recommender systems,online doctor recommendation prioritizes precise matching between patients’conditions and doctors’specialties.Traditional recommendation algorithms are initially applied in doctor recommendation,but they are constrained by data sparsity and cold start.Recommendation based on multi-attribute decision making possess a solid theoretical foundation and can flexibly reflect patient preferences,yet they require a high level of interaction between the system and patients.Recommendation based on machine learning can alleviate the challenge of data sparsity and enable intelligent recommendation,though they necessitate large data support and often suffer from poor interpre-tability.Hybrid recommendation models,by integrating the strengths of various algorithms,have the potential to improve recommendation performance.However,the challenge lies in combining and balancing these algorithms.Other research directions such as recommendation grounded in optimization theory an

关 键 词：在线问诊 医生推荐 推荐算法 机器学习 多属性决策 

分 类 号：C934[经济管理—管理学]