摘要
【目的】针对在线问诊平台医生推荐结果不精确的问题,通过融合多种推荐策略发现优质的医生资源。【方法】通过构建一种基于组合条件的医生推荐模型,其中包括基于相似患者、基于相似领域和基于医生绩效,最后采用线性加权的混合策略整合三种推荐结果,得到最终医生推荐集。同时,为了验证模型的可行性和准确性,采集"好大夫在线"真实数据进行分析。【结果】实验结果表明,经过三种推荐策略的组合,患者当时实际就诊的医生被成功返回的准确率高达86%,说明该模型具有良好的应用可行性和较好的推荐效果。【局限】目标用户的选择容易受随机结果影响;在医生混合推荐结果分析中,每种推荐策略的权重设置比较粗略。【结论】基于组合条件的医生推荐模型,从不同角度挖掘医生特征,有利于帮助患者快速识别并选择优质的医生资源。
[Objective] This paper integrates multiple recommendation strategies to discover high-quality doctor services, aiming to improve the recommendation results from medical consultation websites. [Methods] We built a doctor recommendation model based on combined conditions, which included three models for similar patients,medical fields and doctor performance. Then, we used a linear weighted hybrid strategy to merge these results to create a final list. We retrieved data from"Good Doctor Online"to evaluate the proposed model. [Results] Up to 86% of the doctors seen by the patients were identified by our new model. [Limitations] The choice of users might be affected by random factors and the weight setting of each strategy needs to be improved. [Conclusions]The proposed model could effectively recommend high-quality doctors for patients.
作者
李跃艳
熊回香
李晓敏
Li Yueyan;Xiong Huixiang;Li Xiaomin(School of Information Management,Central China Normal University,Wuhan 430079,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2020年第8期130-141,共12页
Data Analysis and Knowledge Discovery
基金
华中师范大学中央高校基本科研业务费(人文社科类)重大项目“基于语义网的在线健康信息的挖掘与推荐研究”(项目编号:CCNU19Z02004)的研究成果之一。
