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数据分析与知识发现  2020, Vol. 4 Issue (4): 34-43    DOI: 10.11925/infotech.2096-3467.2019.0815
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于Labeled-LDA模型的在线医疗专家推荐研究*
潘有能(),倪秀丽
浙江大学公共管理学院 杭州 310058
Recommending Online Medical Experts with Labeled-LDA Model
Pan Youneng(),Ni Xiuli
School of Public Affairs, Zhejiang University, Hangzhou 310058, China
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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Labeled-LDA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。

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潘有能
倪秀丽
关键词 Labeled-LDA专家推荐主题模型在线医疗    
Abstract

[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.

Key wordsLabeled-LDA    Expert Recommendation    Topic Model    Online Healthcare
收稿日期: 2019-07-12     
中图分类号:  G350  
基金资助:*本文系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”的研究成果之一(13ZJQN043YB)
通讯作者: 潘有能     E-mail: ynpan@zju.edu.cn
引用本文:   
潘有能,倪秀丽. 基于Labeled-LDA模型的在线医疗专家推荐研究*[J]. 数据分析与知识发现, 2020, 4(4): 34-43.
Pan Youneng,Ni Xiuli. Recommending Online Medical Experts with Labeled-LDA Model. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2019.0815.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2019.0815
图1  在线医疗专家推荐模型框架
图2  内科医生主题分布样例
图3  部分健康问题主题分布
组别 准确率 召回率 MRR
组1 42% 41% 0.325
组2 37% 38% 0.301
组3 43% 42% 0.283
组4 35% 34% 0.247
组5 46% 44% 0.362
组6 43% 41% 0.344
组平均值 41% 40% 0.312
测试集总体情况 40% 44% 0.314
表1  在线医疗专家推荐测评结果
组别 组1 组2 组3 组4 组5 组6 组平均值 总体情况
最佳推荐个数 273 240 280 228 300 279 267 1 588
表2  最佳推荐个数测评结果
对比指标 网站现有指标 专家推荐方法
内科健康问题总数 407 189 6 000
内科医生回答采纳次数 27 726 1 588
所有医生回答总次数 1 371 877 14 022
内科医生回答总次数 407 949 6 940
准确率 20.4% 40.4%
召回率 29.7% 44.0%
回答采纳比 6.8% 22.9%
表3  推荐方法对比
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