Personalized Recommendation System for Medical Assistance using Hybrid Filtering

Salunke, Archana B.; Kasar, Smita

doi:10.5120/ijca2015906626

Cited by 2 publications

(1 citation statement)

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“…However, how patients evaluate a particular doctor has not been mentioned. The recommender system presented by Salunke and Kasar [15] generates recommendations based on user ratings. The proposed method used natural language processing and user ratings to construct a doctor profiler.…”

Section: Introductionmentioning

confidence: 99%

A Doctor Recommendation Based on Graph Computing and LDA Topic Model

Meng¹,

Xiong²

2021

IJCIS

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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.

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Section: Introductionmentioning

confidence: 99%