International Journal of Computational Intelligence Systems

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Volume 14, Issue 1, 2021, Pages 808 - 817

A Doctor Recommendation Based on Graph Computing and LDA Topic Model

Authors
Qiuqing Meng1, 2, *, ORCID, Huixiang Xiong1
1School of Information Management, Central China Normal University, Wuhan, 403792, P.R. China
2School of Information, Financial and Economics of Guizhou University, Guiyang, 550025, P.R. China
*Corresponding author. Email: 394125014@qq.com
Corresponding Author
Qiuqing Meng
Received 30 September 2020, Accepted 31 January 2021, Available Online 12 February 2021.
DOI
10.2991/ijcis.d.210205.002How to use a DOI?
Keywords
Doctor recommendation; LDA topic model; Eigenvector centrality; Graph computing; Word2vec
Abstract

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. To 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 consultations between doctors and patients. Then, the model treats these doctors as nodes in order to construct a doctor tag cooccurrence network and recommends the most important doctors in the network via an eigenvector centrality calculation model on the graph. This method identifies the important nodes in the entire effective doctor network to support the recommendation from a new graph computing perspective. An experiment conducted on the Chinese healthcare website Chunyuyisheng.com proves that the proposed method a good recommendation performance.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
14 - 1
Pages
808 - 817
Publication Date
2021/02/12
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.210205.002How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Qiuqing Meng
AU  - Huixiang Xiong
PY  - 2021
DA  - 2021/02/12
TI  - A Doctor Recommendation Based on Graph Computing and LDA Topic Model
JO  - International Journal of Computational Intelligence Systems
SP  - 808
EP  - 817
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210205.002
DO  - 10.2991/ijcis.d.210205.002
ID  - Meng2021
ER  -