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Health level classification by fusing medical evaluation from multiple social networks

机译：通过融合多个社交网络的医学评估来分类健康水平分类

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摘要

Accurate prediction of the health level of a user is a useful and promising technique involving many domains, such as medical analysis, user behavior understanding, industrial design, and human-computer interaction (HC1). User health level can be collaboratively determined by the interactions of physical/cyber companions within the same medical circle, since humans are generally considered social animals. Based on this observation, we propose a novel model for user health level categorization based on the intelligent fusion of medical evaluation results derived from multiple medically-aware social networks. More specifically, given the massive scale of Internet users, we are able to extract certain types of medical features to characterize user's attributes from multiple sources. Initially, we construct a multiple affinity graph to describe a vast number of users' relationships with each attribute. Afterwards, a graph-based clustering is conducted to group these massive number of users into multiple medically-aware social clusters. Based on these clusters, each user's health status is determined by the user's classified health level associated with his/her companions. Lastly, in order to combine the user health level from multiple attributes, a new multi-view medical attribute learning framework is presented. This framework automatically calculates the weight of each attribute. Comprehensive comparative studies on a real-world medical data set with millions of users have demonstrated the superiority and robustness of our approach.
机译：准确预测用户的健康水平是涉及许多域的有用和有希望的技术，例如医学分析，用户行为理解，工业设计和人机交互（HC1）。用户健康水平可以通过同一医学圈内的物理/网络伴侣的相互作用协作确定，因为人类通常被认为是社会动物。基于该观察，我们提出了一种基于来自多个医学媒体的智能融合，提出了一种用于用户健康级别分类的新颖模型，该智能融合来自多个医学意识的社交网络的医学评估结果。更具体地，鉴于互联网用户的大规模，我们能够提取某些类型的医疗功能，以表征来自多个源的用户的属性。最初，我们构建多个亲和图来描述大量用户的关系与每个属性。之后，进行图形的聚类，以将这些大量的用户分组到多个医学上感知的社交集群中。基于这些群集，每个用户的健康状况由用户的分类健康级别与他/她的同伴相关联。最后，为了将用户健康级别与多个属性组合，呈现了一个新的多视图医疗属性学习框架。此框架自动计算每个属性的权重。与数百万用户的现实世界医疗数据的全面比较研究表明了我们方法的优越性和稳健性。

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