Individual Doctor Recommendation in Large Networks by Constrained Optimization

Individual Doctor Recommendation in Large Networks by Constrained Optimization

Jibing Gong (School of Information Science and Engineering, The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, China), Hong Cheng (Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China) and Lili Wang (School of Information Science and Engineering, The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, China)
Copyright: © 2015 |Pages: 13
DOI: 10.4018/IJWSR.2015100102
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Abstract

In this paper, the authors try to systematically investigate the problem of individual doctor recommendation and propose a novel method to enable patients to access such intelligent medical service. In their method, the authors first mine doctor-patient ties/relationships via Time-constraint Probability Factor Graph model (TPFG) from a medical social network. Next, they design a constraint-based optimization framework to efficiently improve the accuracy for doctor-patient relationship mining. Last, they propose a novel Individual Doctor Recommendation Model, namely IDR-Model, to compute doctor recommendation success rate based on weighted average method. The authors conduct experiments to verify the method on a real medical data set. Experimental results show that they obtain better accuracy of mining doctor-patient relationship from the network, and doctor recommendation results of IDR-Model are reasonable and satisfactory.
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Traditional information retrieval models, such as Boolean Model (Ricardo, 1999), Multi-keyword method (Chen, 2012) and Vector Space Model (Salton, 1975), all compute similarity degree between query keywords and destination doctors. A closely related research topic is expertise search, such as expertise search based on candidate vote by Macdonald and Ounis (Macdonald, 2006), expertise mining from social network by Tang et al.(Tang, 2009), and transfer learning from expertise search to Bole search by Yang et al. (Heidelberger, 2011). The probabilistic topic models are often used in social ties/relationships analysis and mining (Zheng, 2012). Bloom Filter based distributed random replication scheme is proposed for content retrieval in unstructured P2P networks (Chen, 2012). Several recent studies focus on how to model the imbalanced and noisy data in order to improve relationships mining performance (Yuan, 2012; Zhou, 2009), how to search in P2P Networks (Chen, 2009; Chen, 2010) and how to obtain better classification results (Zhang, 2009).

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