社会化媒体环境下电子健康知识挖掘研究

项目名称： 社会化媒体环境下电子健康知识挖掘研究

项目编号： No.71471064

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 管理科学

项目作者： 刘璇

作者单位： 华东理工大学

项目金额： 60万元

中文摘要： 随着互联网的发展和人们健康诉求的日益增长，电子健康已成为信息技术在医疗保健领域最重要的革命，而社会化媒体则成为电子健康一个新兴的应用领域。本项目综合运用管理学、医学、社会学、计算机科学和统计学的相关知识，采用算法设计、系统设计、数据挖掘、文本挖掘、社会网络分析等研究方法，从基础数据挖掘（数据层）、社区参与者情感分析与角色识别（个体层）、健康热点主题识别与知识挖掘（社区层）以及公共健康服务决策（社会层）四个层次对社会化媒体环境下电子健康知识挖掘问题展开研究。本项目为健康数据挖掘研究提供了新的视角，为健康社区个体行为分析、健康热点识别、医疗知识挖掘及健康服务监控提供了新方法和手段，对其他领域基于社会媒体的挖掘研究也具有积极的学术意义。同时，本项目的研究结果对于帮助广大网民作出正确的医疗健康决策，促进在线健康网站提高服务质量，提升公共卫生服务机构监管能力，具有重要的现实意义。

中文关键词： 电子健康；情感分析；主题建模；关联规则挖掘；信息采纳与扩散

英文摘要： With the development of Internet and increasing demands of public health, e-health has become an important revolution incorporating information technology into healthcare field, and health social media has become an emerging application context in e-health domain. Leveraging theories in design science, medical science, sociology, computer science, and statistics, we use algorithm design, system design, data mining, text mining, and social network analysis methods to address four levels of knowledge discovery problems in health social media: meta-data mining (data level), sentiment analysis and role identification for community participants (individual level), health hotspot identification and knowledge discovery (community level), as well as public health decision making (society level). This project will provide a new perspective for health mining problems, supply a series of methods and tools for individual behavior analysis, health topic modeling, health knowledge discovery, and public health decision making, meanwhile it will shed light on data mining problems in other domain under social media context. The project also has important practical significance since it is beneficial for empowering health community participants in efficiently decision making, facilitating health social media sites to improve service quality, as well improving surveillance capacity for public health services departments.

英文关键词： E-Health;Sentiment Analysis;Topic modeling;Association Rules Mining;Information Adoption and Diffusion