[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.
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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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