% 0期刊文章% @ 1438 - 8871 V %我JMIR出版物I卡塔尔世界杯8强波胆分析nc . % 16% 11% N P e250 % T tweet的可靠性作为补充的季节性流感监测方法%:Anoshe %祖文萃,Ming-Hsiang % Spitzberg,布莱恩·H %一个李% Gawron, J Gupta,马克% K % Peddecord迪帕克,迈克尔% K内格尔,安娜·C %艾伦Christopher %, Jiue-An %林赛,苏珊娜% +地理系,圣地亚哥州立大学的风暴大厅313 C, 5500年钟楼,圣地亚哥,92115年,美国,1 619 594 0205, mtsou@mail.sdsu.edu %K推特%K推文%K信息监测%K信息流行病学%K症状监测%K流感%K互联网%D 2014 %7 14.11.2014 %9原始论文%J J医学互联网Res %G英文%X背景:美国现有的流感监测主要集中在从哨点医生和医院收集数据;但是，编写和分发报告的工作通常要推迟两个星期。随着社交媒体的日益普及，互联网由于可获得大量数据而成为综合征监测的来源。在这项研究中，从Twitter网站上收集了140个字符或更少的推文，并分析了它们作为季节性流感监测的潜力。目的:本研究有三个目的:(1)通过过滤和机器学习分类器提高推文与各城市哨点提供的流感样疾病(ILI)率的相关性;(2)观察推文与各城市急诊部门ILI率的相关性;(3)探索推文与圣地亚哥实验室确认的流感病例的相关性。方法:从11个美国城市的17英里半径内收集包含“流感”关键字的推文，这些城市被选为人口和ILI数据的可用性。在收集期结束时，使用159,802条推文与哨兵提供的ILI和相应市或县卫生部门报告的急诊ILI率进行相关性分析。研究人员使用了两种不同的方法来观察推文和ILI率之间的相关性:按类型过滤推文(非转发、转发、有URL的推文、没有URL的推文)，以及使用机器学习分类器来确定推文是“有效的”，还是来自可能患了流感的用户。结果:相关性因城市而异，但观察到总体趋势。 Non-retweets and tweets without a URL had higher and more significant (P<.05) correlations than retweets and tweets with a URL. Correlations of tweets to emergency department ILI rates were higher than the correlations observed for sentinel-provided ILI for most of the cities. The machine-learning classifier yielded the highest correlations for many of the cities when using the sentinel-provided or emergency department ILI as well as the number of laboratory-confirmed influenza cases in San Diego. High correlation values (r=.93) with significance at P<.001 were observed for laboratory-confirmed influenza cases for most categories and tweets determined to be valid by the classifier. Conclusions: Compared to tweet analyses in the previous influenza season, this study demonstrated increased accuracy in using Twitter as a supplementary surveillance tool for influenza as better filtering and classification methods yielded higher correlations for the 2013-2014 influenza season than those found for tweets in the previous influenza season, where emergency department ILI rates were better correlated to tweets than sentinel-provided ILI rates. Further investigations in the field would require expansion with regard to the location that the tweets are collected from, as well as the availability of more ILI data. %M 25406040 %R 10.2196/jmir.3532 %U //www.mybigtv.com/2014/11/e250/ %U https://doi.org/10.2196/jmir.3532 %U http://www.ncbi.nlm.nih.gov/pubmed/25406040