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

所属专题： Artificial Intelligence and Pattern Recognition； Data Management and Data Mining

• Special Section on Selected Paper from NPC 2011 • 上一篇    下一篇

医疗社会网络中一种混合多层医生推荐架构的研究

Ji-Bing Gong(宫继兵), Member, CCF, Li-Li Wang*(王立立), Student Member, CCF Sheng-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
  • 收稿日期:2014-11-17 修回日期:2015-06-15 出版日期:2015-09-05 发布日期:2015-09-05
  • 通讯作者: Li-Li Wang E-mail:wanglili_ysu@163.com
  • 作者简介: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.
  • 基金资助:

    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.

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.

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随着经济的发展, 人们越来越重视自身的健康状况, 然而, 由于医疗资源严重受限, 大部分病人很难找到一个合适的医生进行诊断。在实际生活中, 医疗健康网络扮演着越来越重要的作用。本文尝试系统地研究如何在社会医疗网络(medical social networks, 简称MSNs)中进行医生推荐。具体来说是提出了一个新的混合多层的医生推荐架构来解决这个问题。作为一个新的热点课题, 医生推荐研究存在以下挑战, 分别是:如何挖掘医患关系, 怎样进行医生推荐以及怎样评估所提的推荐模型的精度。为了解决以上挑战, 我们尝试系统的调研医生推荐问题, 即:在医疗社会网络中设计一个统一的医生推荐架构, 系统地分析并抽取真实医疗社会网络的特征, 最后提出基于随机游走模型的医生推荐模型(RWR-Model), 本文的研究贡献如下:(1)提出了一个基于时间约束的概率因子图模型(简称TPFG)来挖掘"医生-病人"关系;(2)考虑到医生推荐需求, 定义并形成了四个网络特征, 然后抽取这四个特征;(3)提出了一个创新的混合多层架构以解决医生推荐问题。在此架构中提出了使用随机游走模型(RWR-Model)的医生推荐, 并根据信息检索指标评估推荐精度。通过真实实验验证了所提方法的有效性。实验结果表明本文方法能从网络中获得很好的医患关系挖掘精度, 所提出的RWR-Model医生推荐模型的性能也比传统Ranking SVM和个性化医生推荐模型等基线算法都要好。

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.

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