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Journal of Computer Science and Technology

, Volume 30, Issue 5, pp 1073–1081 | Cite as

iBole: A Hybrid Multi-Layer Architecture for Doctor Recommendation in Medical Social Networks

  • Ji-Bing Gong
  • Li-Li Wang
  • Sheng-Tao Sun
  • Si-Wei Peng
Regular Paper

Abstract

In this paper, we try to systematically study how to perform doctor recommendation in medical social networks (MSNs). Specifically, employing a real-world medical dataset as the source in our work, we propose iBole, a novel hybrid multi-layer architecture, to solve this problem. First, we mine doctor-patient relationships/ties via a time-constraint probability factor graph model (TPFG). Second, we extract network features for ranking nodes. Finally, we propose RWRModel, a doctor recommendation model via the random walk with restart method. Our real-world experiments validate the effectiveness of the proposed methods. Experimental results show that we obtain good accuracy in mining doctor-patient relationships from the network, and the doctor recommendation performance is better than that of the baseline algorithms: traditional Ranking SVM (RSVM) and the individual doctor recommendation model (IDR-Model). The results of our RWR-Model are more reasonable and satisfactory than those of the baseline approaches.

Keywords

doctor recommendation architecture random walk with restart doctor-patient tie mining time-constraint probability factor graph model medical social network 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Ji-Bing Gong
    • 1
    • 2
  • Li-Li Wang
    • 1
    • 2
  • Sheng-Tao Sun
    • 1
    • 2
  • Si-Wei Peng
    • 1
    • 2
  1. 1.School of Information Science and EngineeringYanshan UniversityQinhuangdaoChina
  2. 2.The Key Laboratory for Computer Virtual Technology and System Integration of Hebei ProvinceYanshan UniversityQinhuangdaoChina

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