摘要
【目的】改进现有在线医疗专家推荐模型,提高医生回答健康问题的效率和质量。【方法】基于LabeledLDA模型挖掘健康问题潜在主题,明确医生专长,以提高"问题-医生"匹配度,并使用39健康网的数据进行实验验证。【结果】本文方法的准确率、召回率和回答采纳比分别为40.4%、44.0%和22.9%,而网站现有指标分别为20.4%、29.7%和6.8%。【局限】未考虑医生回答问题的速度和医生的简历等相关信息;不能很好地识别出回答问题过于稀疏的新加入医生的专长。【结论】本研究所提专家推荐方法在评价指标上均超过网站现有指标,具有良好的推荐效果。
[Objective] This paper tries to modify the existing recommendation model for online medical experts,aiming to more effectively address health-related inquiries. [Methods] First, we identified the latent topics of online health questions with the help of Labeled-LDA model. Then, we defined the doctors’ specialties and better match them with questions. Finally, we evaluated the new model with data from http://www. 39. net. [Results]The precision, recall and response adoption rates of the proposed method were 40. 4%, 44. 0% and 22. 9%, which were much higher than those of the existing ones. [Limitations] Our method did not include factors like doctors’ responding time and their resumes. This method could not identify expertise of newly joined doctors who answered few questions. [Conclusions] The proposed model could effectively recommend physicians for patients asking questions online.
作者
潘有能
倪秀丽
Pan Youneng;Ni Xiuli(School of Public Affairs,Zhejiang University,Hangzhou 310058,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2020年第4期34-43,共10页
Data Analysis and Knowledge Discovery
基金
浙江省哲学社会科学规划项目“基于领域本体的知识地图构建研究”(项目编号:13ZJQN043YB)的研究成果之一。
