A Doctor Recommendation Based on Graph Computing and LDA Topic Model

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 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. (C) 2021 The Authors. Published by Atlantis Press B.V.

Automated summary

Doctor recommendation technology utilizes a hybrid model and graph computing methods to help patients quickly and accurately find doctors who meet their actual needs based on consultation information, providing helpful personalized online healthcare services.