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

作　　者：宫继兵 王立立 孙胜涛 彭思维 

机构地区：[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

出　　处：《Journal of Computer Science & Technology》2015年第5期1073-1081,共9页计算机科学技术学报（英文版）

基　　金：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.

摘　　要：In this paper, we try to systematically study how to perform doctor recommendation in medical social net- works （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 RWR- Model, 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.In this paper, we try to systematically study how to perform doctor recommendation in medical social net- works （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 RWR- Model, 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.

关 键 词：doctor recommendation architecture random walk with restart doctor-patient tie mining time-constraintprobability factor graph model medical social network 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] TS974.1[自动化与计算机技术—计算机科学与技术]