Twitter mining for fine-grained syndromic surveillance
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摘要
BackgroundDigital traces left on the Internet by web users, if properly aggregated and analyzed, can represent a huge information dataset able to inform syndromic surveillance systems in real time with data collected directly from individuals. Since people use everyday language rather than medical jargon (e.g. runny nose vs. respiratory distress), knowledge of patients’ terminology is essential for the mining of health related conversations on social networks.
论文关键词：Terminology clustering,Twitter mining,Micro-blog mining,Patient's language learning,Syndromic surveillance
论文评审过程：Available online 31 January 2014.
论文官网地址：https://doi.org/10.1016/j.artmed.2014.01.002
