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A Doctor Recommendation Based on Graph Computing and LDA Topic Model

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成果类型:
期刊论文
作者:
Meng, Qiuqing*;Xiong, Huixiang(熊回香
通讯作者:
Meng, Qiuqing
作者机构:
[Xiong, Huixiang; Meng, Qiuqing] Cent China Normal Univ, Sch Informat Management, Wuhan 403792, Peoples R China.
[Meng, Qiuqing] Guizhou Univ, Sch Informat Financial & Econ, Guiyang 550025, Peoples R China.
通讯机构:
[Meng, Qiuqing] C
[Meng, Qiuqing] G
Cent China Normal Univ, Sch Informat Management, Wuhan 403792, Peoples R China.
Guizhou Univ, Sch Informat Financial & Econ, Guiyang 550025, Peoples R China.
语种:
英文
关键词:
Doctor recommendation;LDA topic model;Eigenvector centrality;Graph computing;word2vec
期刊:
International Journal of Computational Intelligence Systems
ISSN:
1875-6891
年:
2021
卷:
14
期:
1
页码:
808-817
基金类别:
National Social Science Foundation of China [19BTQ005]; Scientific research projects Foundation of Financial and Economics of Guizhou University [2019XYB03]
机构署名:
本校为第一且通讯机构
院系归属:
信息管理学院
摘要:
Doctor recommendation technology can help patients filter out large number of irrelevant doctors and find doctors who meet their actual needs quickly and accurately, helping patients gain access to helpful personalized online healthcare services. 'co address the problems with the existing recommendation methods, this paper proposes a hybrid doctor recommendation model based on online healthcare platform, which utilizes the word2vec model, latent Dirichlet allocation (LDA) topic model, and other methods to find doctors who best suit patients' needs with the information obtained from consultatio...

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