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    基于概率因子图模型的医疗社会网络用户健康状态检测方法

    Health Status Detection via Temporal-Spatial Factor Graph Model in Medical Social Networks

    • 摘要: 社会网络应用已无处不在,在健康医疗领域也是如此.同时,传感器网络的发展也面临新的形势.在真实世界中,有许多因素(如社会关系、历史健康状态和个人属性信息)都能对健康状态检测/预测结果产生影响.然而,却很少有相关文献能够系统阐述新形势下在一个动态社会网络中节点用户健康状态如何进行检测/预测以及不同因素对用户健康状态影响到何种程度.首先描述一种新颖的医疗物联网:医疗社会网络(medical social networks, MSNs);然后统一考虑社会关系、历史健康状态和用户属性对网络用户健康状态检测结果的影响,提出一种新的基于时-空概率因子图模型(temporal-spatial factor graph model, TS-FGM)的网络用户健康状态检测/预测方法.在Twitter数据集上对所提出的模型进行了验证,并在一个真实的临床医疗数据集上与SVM基线算法进行了对比实验.实验结果表明所提出的TS-FGM模型是有效的,健康状态检测方法也在一定程度上优于基线方法.

       

      Abstract: Applications of Social Networks is very popular in various fields including health and medical area. At the same time, Wireless Sensor Networks(WSNs) has some new development situations. In real world, people’s health status detection/prediction is influenced by various factors such as social relationships, history health statuses and people’s personal condition. However, few publications systematically study how health statuses evolve in a dynamic social network and to what extent different factors affect the user health status. In this paper, we first describe a novel Medical Social Networks(MSNs) which is a classic kind of Medical Internet of Things(Medical IoTs). Then combining these above factors together, we propose a unified model, namely TS-FGM, based on Probability Factor Graph Model, and thus present a novel health status prediction method based on TS-FGM in MSNs. More specifically, users’ health statuses at time t are influenced by their private attributes, their own health statuses at time t-1 and their neighbors’ health statuses at both time t and t-1. At last, we present an efficient decision-fusion-oritented algorithm to learn the model. Finally, we validate the model on real-world data sets in Twitter. And we compare our method with baseline algorithm SVM on a real clinic medical data set for pulse diagnosis. Experimental results show that the model is effective and the proposed method partly outperforms the baseline method for disease prediction.

       

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