›› 2015, Vol. 30 ›› Issue (5): 1073-1081.doi: 10.1007/s11390-015-1583-5

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Social Media Processing • Previous Articles     Next Articles

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

Ji-Bing Gong(宫继兵), Member, CCF, Li-Li Wang*(王立立), Student Member, CCFSheng-Tao Sun(孙胜涛), Member, CCF, Si-Wei Peng(彭思维)   

  1. 1 School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China;
    2 The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University Qinhuangdao 066004, China
  • Received:2014-11-17 Revised:2015-06-15 Online:2015-09-05 Published:2015-09-05
  • Contact: Li-Li Wang E-mail:wanglili_ysu@163.com
  • About author:Ji-Bing Gong is an associate professor at the School of Information Science and Engineering, Yanshan University, Qinhuangdao. He received his Ph.D. degree in computer architecture from the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, in 2012. He is a member of CCF. His research interests include data mining, social networks, and machine learning.
  • Supported by:

    This work was supported by the the National High Technology Research and Development 863 Program of China under Grant No. 2015AA124102, the Hebei Natural Science Foundation of China under Grant No. F2015203280, and the National Natural Science Foundation of China under Grant Nos. 61303130, 61272466, and 61303233.

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.

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