• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

推特流感监测：量化季节性误诊模式及其对监测估计的影响。

Twitter Influenza Surveillance: Quantifying Seasonal Misdiagnosis Patterns and their Impact on Surveillance Estimates.

作者信息

Mowery Jared

机构信息

The MITRE Corporation.

出版信息

Online J Public Health Inform. 2016 Dec 28;8(3):e198. doi: 10.5210/ojphi.v8i3.7011. eCollection 2016.

DOI:10.5210/ojphi.v8i3.7011
PMID:28210419
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5302465/
Abstract

BACKGROUND

Influenza (flu) surveillance using Twitter data can potentially save lives and increase efficiency by providing governments and healthcare organizations with greater situational awareness. However, research is needed to determine the impact of Twitter users' misdiagnoses on surveillance

OBJECTIVE

This study establishes the importance of Twitter users' misdiagnoses by showing that Twitter flu surveillance in the United States failed during the 2011-2012 flu season, estimates the extent of misdiagnoses, and tests several methods for reducing the adverse effects of misdiagnoses.

METHODS

Metrics representing flu prevalence, seasonal misdiagnosis patterns, diagnosis uncertainty, flu symptoms, and noise were produced using Twitter data in conjunction with OpenSextant for geo-inferencing, and a maximum entropy classifier for identifying tweets related to illness. These metrics were tested for correlations with World Health Organization (WHO) positive specimen counts of flu from 2011 to 2014.

RESULTS

Twitter flu surveillance erroneously indicated a typical flu season during 2011-2012, even though the flu season peaked three months late, and erroneously indicated plateaus of flu tweets before the 2012-2013 and 2013-2014 flu seasons. Enhancements based on estimates of misdiagnoses removed the erroneous plateaus and increased the Pearson correlation coefficients by .04 and .23, but failed to correct the 2011-2012 flu season estimate. A rough estimate indicates that approximately 40% of flu tweets reflected misdiagnoses.

CONCLUSIONS

Further research into factors affecting Twitter users' misdiagnoses, in conjunction with data from additional atypical flu seasons, is needed to enable Twitter flu surveillance systems to produce reliable estimates during atypical flu seasons.

摘要

背景

利用推特数据进行流感监测，可为政府和医疗组织提供更强的态势感知，从而有可能挽救生命并提高效率。然而，需要开展研究来确定推特用户误诊对监测的影响。

目的

本研究通过表明美国2011 - 2012流感季期间推特流感监测失败，来确定推特用户误诊的重要性，估计误诊程度，并测试几种减少误诊负面影响的方法。

方法

结合使用推特数据、用于地理推理的OpenSextant以及用于识别与疾病相关推文的最大熵分类器，生成代表流感流行率、季节性误诊模式、诊断不确定性、流感症状和噪音的指标。对这些指标与世界卫生组织（WHO）2011年至2014年流感阳性样本计数的相关性进行测试。

结果

推特流感监测错误地表明2011 - 2012年是典型的流感季，尽管该流感季高峰推迟了三个月，并且在2012 - 2013年和2013 - 2014年流感季之前错误地显示流感推文处于平稳期。基于误诊估计的改进消除了错误的平稳期，使皮尔逊相关系数分别提高了0.04和0.23，但未能纠正2011 - 2012年流感季的估计。粗略估计表明，约40%的流感推文反映了误诊。

结论

需要进一步研究影响推特用户误诊的因素，并结合其他非典型流感季的数据，以使推特流感监测系统能够在非典型流感季产生可靠的估计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef92/5302465/084ae1eced63/ojphi-08-e198-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef92/5302465/aa880d0208a7/ojphi-08-e198-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef92/5302465/ef5b1f3541e2/ojphi-08-e198-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef92/5302465/aea1c5cfafea/ojphi-08-e198-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef92/5302465/5d8009732eda/ojphi-08-e198-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef92/5302465/709dec27db1d/ojphi-08-e198-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef92/5302465/084ae1eced63/ojphi-08-e198-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef92/5302465/aa880d0208a7/ojphi-08-e198-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef92/5302465/ef5b1f3541e2/ojphi-08-e198-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef92/5302465/aea1c5cfafea/ojphi-08-e198-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef92/5302465/5d8009732eda/ojphi-08-e198-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef92/5302465/709dec27db1d/ojphi-08-e198-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef92/5302465/084ae1eced63/ojphi-08-e198-g006.jpg

相似文献

1
Twitter Influenza Surveillance: Quantifying Seasonal Misdiagnosis Patterns and their Impact on Surveillance Estimates.推特流感监测：量化季节性误诊模式及其对监测估计的影响。
Online J Public Health Inform. 2016 Dec 28;8(3):e198. doi: 10.5210/ojphi.v8i3.7011. eCollection 2016.
2
The reliability of tweets as a supplementary method of seasonal influenza surveillance.推文作为季节性流感监测补充方法的可靠性。
J Med Internet Res. 2014 Nov 14;16(11):e250. doi: 10.2196/jmir.3532.
3
Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data.加强季节性流感监测：利用推特数据对广泛使用的药物进行主题分析
J Med Internet Res. 2017 Sep 12;19(9):e315. doi: 10.2196/jmir.7393.
4
Mapping tweets to a known disease epidemiology; a case study of Lyme disease in the United Kingdom and Republic of Ireland.将推文映射到已知疾病流行病学；英国和爱尔兰共和国莱姆病的案例研究。
J Biomed Inform. 2019;100S:100060. doi: 10.1016/j.yjbinx.2019.100060. Epub 2019 Oct 18.
5
Social media and flu: Media Twitter accounts as agenda setters.社交媒体与流感：作为议程设置者的媒体推特账号
Int J Med Inform. 2016 Jul;91:67-73. doi: 10.1016/j.ijmedinf.2016.04.009. Epub 2016 Apr 22.
6
Can online self-reports assist in real-time identification of influenza vaccination uptake? A cross-sectional study of influenza vaccine-related tweets in the USA, 2013-2017.在线自我报告能否有助于实时识别流感疫苗接种情况？2013-2017 年美国与流感疫苗相关推文的横断面研究。
BMJ Open. 2019 Jan 15;9(1):e024018. doi: 10.1136/bmjopen-2018-024018.
7
Modeling the impact of twitter on influenza epidemics.模拟推特对流感流行的影响。
Math Biosci Eng. 2014 Dec;11(6):1337-56. doi: 10.3934/mbe.2014.11.1337.
8
Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study.利用社交媒体在内城医院开展本地流感监测：一项回顾性观察研究。
JMIR Public Health Surveill. 2015 Jan-Jun;1(1):e5. doi: 10.2196/publichealth.4472. Epub 2015 May 29.
9
Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza.应用地理信息系统和机器学习方法于推特数据以进行流感的多尺度监测。
PLoS One. 2016 Jul 25;11(7):e0157734. doi: 10.1371/journal.pone.0157734. eCollection 2016.
10
Characterizing the Discussion of Antibiotics in the Twittersphere: What is the Bigger Picture?剖析推特上关于抗生素的讨论：整体情况如何？
J Med Internet Res. 2015 Jun 19;17(6):e154. doi: 10.2196/jmir.4220.

引用本文的文献

1
Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic.支持新冠疫情背景下监测策略的数据与数字解决方案
Front Digit Health. 2021 Aug 6;3:707902. doi: 10.3389/fdgth.2021.707902. eCollection 2021.
2
Automatic gender detection in Twitter profiles for health-related cohort studies.用于健康相关队列研究的推特个人资料中的自动性别检测
JAMIA Open. 2021 Jun 23;4(2):ooab042. doi: 10.1093/jamiaopen/ooab042. eCollection 2021 Apr.
3
Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach.

本文引用的文献

1
Review Article: Influenza Transmission on Aircraft: A Systematic Literature Review.综述文章：飞机上的流感传播：一项系统的文献综述。
Epidemiology. 2016 Sep;27(5):743-51. doi: 10.1097/EDE.0000000000000438.
2
Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance.基于云的电子健康记录用于实时、特定地区的流感监测。
Sci Rep. 2016 May 11;6:25732. doi: 10.1038/srep25732.
3
Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study.利用社交媒体在内城医院开展本地流感监测：一项回顾性观察研究。
利用电子健康记录、网络和气候数据源，结合机器学习集成方法对法国地区进行流感预测。
PLoS One. 2021 May 19;16(5):e0250890. doi: 10.1371/journal.pone.0250890. eCollection 2021.
4
Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review.参与式健康信息学在大流行病的检测和管理中的作用：文献回顾。
Yearb Med Inform. 2021 Aug;30(1):200-209. doi: 10.1055/s-0041-1726486. Epub 2021 Apr 21.
5
Social media effectiveness as a humanitarian response to mitigate influenza epidemic and COVID-19 pandemic.社交媒体作为减轻流感疫情和新冠疫情的人道主义应对措施的有效性。
Ann Oper Res. 2022;319(1):823-851. doi: 10.1007/s10479-021-03955-y. Epub 2021 Jan 29.
6
Prospective associations of regional social media messages with attitudes and actual vaccination: A big data and survey study of the influenza vaccine in the United States.前瞻性关联研究：社交媒体区域消息与态度和实际疫苗接种的关系——美国流感疫苗的大数据和调查研究。
Vaccine. 2020 Sep 11;38(40):6236-6247. doi: 10.1016/j.vaccine.2020.07.054. Epub 2020 Aug 10.
7
Social media based surveillance systems for healthcare using machine learning: A systematic review.基于社交媒体的机器学习医疗保健监测系统：一项系统综述。
J Biomed Inform. 2020 Aug;108:103500. doi: 10.1016/j.jbi.2020.103500. Epub 2020 Jul 2.
8
Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data.利用自然语言处理通过社交媒体和消费者生成的数据解决公共卫生研究问题的最新进展
Yearb Med Inform. 2019 Aug;28(1):208-217. doi: 10.1055/s-0039-1677918. Epub 2019 Aug 16.
9
Increasing Interest of Mass Communication Media and the General Public in the Distribution of Tweets About Mental Disorders: Observational Study.大众传播媒体和公众对有关精神障碍推文传播的兴趣日益增加：观察性研究。
J Med Internet Res. 2018 May 28;20(5):e205. doi: 10.2196/jmir.9582.
10
Social Media as a New Vital Sign: Commentary.社交媒体作为一项新的生命体征：评论
J Med Internet Res. 2018 Apr 30;20(4):e161. doi: 10.2196/jmir.8563.
JMIR Public Health Surveill. 2015 Jan-Jun;1(1):e5. doi: 10.2196/publichealth.4472. Epub 2015 May 29.
4
Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance.结合搜索、社交媒体和传统数据源以改善流感监测。
PLoS Comput Biol. 2015 Oct 29;11(10):e1004513. doi: 10.1371/journal.pcbi.1004513. eCollection 2015 Oct.
5
Age-related differences in the accuracy of web query-based predictions of influenza-like illness.基于网络查询的流感样疾病预测准确性的年龄相关差异。
PLoS One. 2015 May 26;10(5):e0127754. doi: 10.1371/journal.pone.0127754. eCollection 2015.
6
Estimating the secondary attack rate and serial interval of influenza-like illnesses using social media.利用社交媒体估算流感样疾病的二代发病率和传播间隔时间。
Influenza Other Respir Viruses. 2015 Jul;9(4):191-9. doi: 10.1111/irv.12321.
7
Automatic detection of tweets reporting cases of influenza like illnesses in Australia.自动检测澳大利亚报告流感样病例的推文。
Health Inf Sci Syst. 2015 Feb 24;3(Suppl 1 HISA Big Data in Biomedicine and Healthcare 2013 Con):S4. doi: 10.1186/2047-2501-3-S1-S4. eCollection 2015.
8
Computational approaches to influenza surveillance: beyond timeliness.流感监测的计算方法：超越及时性
Cell Host Microbe. 2015 Mar 11;17(3):275-278. doi: 10.1016/j.chom.2015.02.004.
9
Inference of seasonal and pandemic influenza transmission dynamics.季节性流感和大流行性流感传播动力学的推断
Proc Natl Acad Sci U S A. 2015 Mar 3;112(9):2723-8. doi: 10.1073/pnas.1415012112. Epub 2015 Feb 17.
10
A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives.一项利用2012 - 2013年纽约市流感季节每日地理编码推特数据从时间和时空角度进行的案例研究。
J Med Internet Res. 2014 Oct 20;16(10):e236. doi: 10.2196/jmir.3416.