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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (6): 118-128    DOI: 10.11925/infotech.2096-3467.2019.1156
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Collaborative Tagging for Doctors in Online Medical Community
Ye Jiaxin1,Xiong Huixiang1(),Tong Zhaoli1,2,Meng Qiuqing1
1School of Information Management, Central China Normal University, Wuhan 430079, China
2Hubei Communication Technical College, Wuhan 430079, China
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[Objective] This paper tries to find similar doctors and improve the descriptions of their characteristics. [Methods] We generated vector representation for each doctor’s consulting texts, article titles and service scopes with the Word2Vec model, which helped us identify similar doctors. Then, we analyzed their common characteristics and collaboratively tag these doctors. [Results] The accuracy of tagging results based on doctor’s consulting texts, article titles and services were 0.667, 0.252 and 0.708, respectively. The accuracy of tagging results based on mixed texts was 1.000. [Limitations] The performance of single-text based tagging needs to be improved. [Conclusions] Tags based on consultation texts are closely related to the immediate needs of patients, while tags based on article titles are strongly related to doctor’s interests. Tags obtained from their services and mixed texts are more accurate.

Key wordsWord2Vec      Collaborative Tagging      Physician Tagging      Tag Recommendations     
Received: 22 October 2019      Published: 07 July 2020
ZTFLH:  G206  
Corresponding Authors: Xiong Huixiang     E-mail:

Cite this article:

Ye Jiaxin,Xiong Huixiang,Tong Zhaoli,Meng Qiuqing. Collaborative Tagging for Doctors in Online Medical Community. Data Analysis and Knowledge Discovery, 2020, 4(6): 118-128.

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Sample Data of 800 Doctors
Sample Training Texts of 596 Doctors
对比项 肺癌 肺部结节 肺部疾病 肺炎 糖尿病 不孕不育 呼吸衰竭
序号 1 2 3 4 5 6 204
频次 119 112 108 92 89 84 1
概率 0.200 0.188 0.181 0.154 0.149 0.141 0.002
Vote Data of 596 Doctors from Patient
The Vote Frequency Line Chart of 204 Diseases
Collaborative Tagging Model
The Word Vector of Patial Words
Test Doctor and His Similar Doctor
The Number of Doctors Meeting the Tagging Criteria
投票 出现频次 出现概率 原出现概率 原出现概率×2
糖尿病 4 0.500 0.149 0.298
高血压 3 0.375 0.134 0.268
甲亢 3 0.375 0.129 0.258
甲减 3 0.375 0.112 0.224
内分泌疾病 2 0.250 0.020 0.040
不孕不育 1 0.125 0.141 0.282
乙肝 1 0.125 0.134 0.268
试管婴儿 1 0.125 0.119 0.238
感染 1 0.125 0.015 0.030
Similar Doctor Vote for Test Doctor
Doctor Tagging Based on Different Texts
测试医生 标签
13 高血压;冠心病;心脏病;房颤
21 Null
28 肺炎;咳嗽;哮喘;支气管炎;支气管扩张
35 糖尿病;甲亢;甲减;甲状腺疾病
38 Null
63 哮喘;过敏
Doctor Tagging of Mixed Different Texts
Evaluation of Tagging Effect
[1] 孙国强, 由丽孪, 陈思, 等. 互联网+医疗模式的初步探索[J]. 中国数字医学, 2015,10(6):15-18.
[1] ( Sun Guoqiang, You Liluan, Chen Si, et al. Preliminary Exploration of Internet + Medical Model[J]. China Digital Medicine, 2015,10(6):15-18.)
[2] 高山, 刘炜, 崔勇, 等. 一种融合多种用户行为的协同过滤推荐算法[J]. 计算机科学, 2016,43(9):227-231.
[2] ( Gao Shan, Liu Wei, Cui Yong, et al. Collaborative Filtering Algorithm Integrating Multiple User Behaviors[J]. Computer Science, 2016,43(9):227-231.)
[3] Huang Z X, Lu X D, Duan H L, et al. Collaboration-based Medical Knowledge Recommendation[J]. Artificial Intelligence in Medicine, 2012,55(1):13-24.
doi: 10.1016/j.artmed.2011.10.002
[4] Jelassi M N, Yahia S B, Nguifo E M. Towards More Targeted Recommendations in Folksonomies[J]. Social Network Analysis and Mining, 2015, 5(1): Article No. 68.
doi: 10.1007/s13278-015-0307-8
[5] Bertram R, Schrimpf G, Stamm-Wilbrandt H. System and Method for Item Recommendations: USA, US8700448B2[P]. 2014-04-15.
[6] 熊回香, 杨雪萍. 社会化标注系统中的个性化信息推荐研究[J]. 情报学报, 2016,35(5):549-560.
[6] ( Xiong Huixiang, Yang Xueping. Personalized Information Recommendation Research Based on Combined Condition in Folksonomies[J]. Journal of the China Society for Scientific and Technical Information, 2016,35(5):549-560.)
[7] 李枫林, 陈德鑫, 梁少星. 基于语义关联和情景感知的个性化推荐方法研究[J]. 情报杂志, 2015,34(10):189-195.
[7] ( Li Fenglin, Chen Dexin, Liang Shaoxing. Research on Personalized Recommendation Method Based on Semantic Association and Context Awareness[J]. Journal of Intelligence, 2015,34(10):189-195.)
[8] Chawda V L, Mahalle V S. Learning to Recommend Descriptive Tags for Health Seekers Using Deep Learning [C]//Proceedings of the 2017 International Conference on Inventive Systems and Control (ICISC). IEEE, 2017: 1-7.
[9] Qassimi S, Abdelwahed E H, Hafidi M, et al. A Graph-Based Model for Tag Recommendations in Clinical Decision Support System [C]//Proceedings of the 8th International Conference on Model and Data Engineering. Springer, 2018: 292-300.
[10] Qassimi S, Abdelwahed E H, Hafidi M, et al. The Role of Recommender System of Tags in Clinical Decision Support [C]// Proceedings of the 2018 International Conference on Advanced Intelligent Systems for Sustainable Development. Springer, 2018: 273-285.
[11] 魏建良, 朱庆华. 社会化标注理论研究综述[J]. 中国图书馆学报, 2009,35(6):88-96.
[11] ( Wei Jianliang, Zhu Qinghua. A Review of the Study of Social Tagging Theory[J]. Journal of Library Science in China, 2009,35(6):88-96.)
[12] 向菲, 彭昱欣, 邰杨芳. 一种基于协同过滤的图书资源标签推荐方法研究[J]. 图书馆学研究, 2018(15):46-52.
[12] ( Xiang Fei, Peng Yuxin, Tai Yangfang. Research on a Book Resource Tag Recommendation Method Based on the Collaborative Filtering[J]. Research on Library Science, 2018(15):46-52.)
[13] 成全. 基于协同标注的科研社区知识融合机制研究[J]. 情报理论与实践, 2011,34(8):20-25.
[13] ( Cheng Quan. Research on the Mechanism of Knowledge Integration in Research-oriented Community Based on Collaborative Annotation[J]. Information Studies: Theory & Application, 2011,34(8):20-25.)
[14] 祝锡永, 周益辉, 李晟. 语义Web环境中基于本体推理的协同标注[J]. 浙江理工大学学报, 2012,29(4):555-559.
[14] ( Zhu Xiyong, Zhou Yihui, Li Sheng. Collaborative Annotation Based on Ontology Reasoning in Semantic Web Environment[J]. Journal of Zhejiang Sci-Tech University, 2012,29(4):555-559.)
[15] 杜红乐, 滕少华, 张燕. 协同标注的直推式支持向量机算法[J]. 小型微型计算机系统, 2016,37(11):2443-2447.
[15] ( Du Hongle, Teng Shaohua, Zhang Yan. Transductive Support Vector Machine Based on Cooperative Labeling[J]. Journal of Chinese Computer Systems, 2016,37(11):2443-2447.)
[16] 杜红乐, 张燕. 基于聚类和协同标注的TSVM算法[J]. 河南科学, 2017,35(1):22-27.
[16] ( Du Hongle, Zhang Yan. Transductive Support Vector Machine Algorithm Based on Cluster and Cooperative Labeling[J]. Henan Science, 2017,35(1):22-27.)
[17] Mikolov T, Sutskever I, Chen K, et al. Distributed Representations of Words and Phrases and Their Compositionality [C]// Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013,2:3111-3119.
[18] 熊富林, 邓怡豪, 唐晓晟. Word2Vec的核心架构及其应用[J]. 南京师范大学学报: 工程技术版, 2015,15(1):43-48.
[18] ( Xiong Fulin, Deng Yihao, Tang Xiaosheng. The Architecture of Word2Vec and Its Applications[J]. Journal of Nanjing Normal University: Engineering and Technology Edition, 2015,15(1):43-48.)
[19] Zhu Y, Yan E, Wang F. Semantic Relatedness and Similarity of Biomedical Terms: Examining the Effects of Recency, Size, and Section of Biomedical Publications on the Performance of Word2Vec[J]. BMC Medical Informatics and Decision Making, 2017, 17: Article No. 95.
doi: 10.1186/1472-6947-12-95 pmid: 22947211
[20] Xu C, Liu D. Chinese Text Summarization Algorithm Based on Word2Vec[C]//Proceedings of the 2018 International Conference on Control Engineering and Artificial Intelligence. IOP Publishing, 2018,976:012006.
[21] 好大夫在线简介[EB/OL]. [2019-07-24].
[21] (An Introduction of “Hao Daifu” [EB/OL]. [2019-07-24]. )
[22] 好大夫在线[EB/OL]. [2019-07-03].
[22] (Hao Daifu[EB/OL]. [2019-07-03]. )
[23] 李心蕾, 王昊, 刘小敏, 等. 面向微博短文本分类的文本向量化方法比较研究[J]. 数据分析与知识发现, 2018,2(8):41-50.
[23] ( Li Xinlei, Wang Hao, Liu Xiaomin, et al. Comparing Text Vector Generators for Weibo Short Text Classification[J]. Data Analysis and Knowledge Discovery, 2018,2(8):41-50.)
[24] 陈梅梅, 薛康杰. 基于改进张量分解模型的个性化推荐算法研究[J]. 数据分析与知识发现, 2017,1(3):38-45.
[24] ( Chen Meimei, Xue Kangjie. Personalized Recommendation Algorithm Based on Modified Tensor Decomposition Model[J]. Data Analysis and Knowledge Discovery, 2017,1(3):38-45.)
[25] 徐文青, 双林平. 融合热门度因子基于标签的个性化图书推荐算法[J]. 图书情报研究, 2015,8(3):82-86.
[25] ( Xu Wenqing, Shuang Linping. Personalized Tag-based Book Recommendation Algorithm Combined with the Factor of Popularity[J]. Library and Information Studies, 2015,8(3):82-86.)
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