Web search activity data accurately predict population chronic disease risk in the USA
Published 2015 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Web search activity data accurately predict population chronic disease risk in the USA
Authors
Keywords
-
Journal
JOURNAL OF EPIDEMIOLOGY AND COMMUNITY HEALTH
Volume 69, Issue 7, Pages 693-699
Publisher
BMJ
Online
2015-03-25
DOI
10.1136/jech-2014-204523
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Regularization Paths for Generalized Linear Models via Coordinate Descent
- (2015) Jerome Friedman et al. Journal of Statistical Software
- Systematic identification of transcriptional and post-transcriptional regulations in human respiratory epithelial cells during influenza A virus infection
- (2014) Zhi-Ping Liu et al. BMC BIOINFORMATICS
- Developing small-area predictions for smoking and obesity prevalence in the United States for use in Environmental Public Health Tracking
- (2014) Alberto M. Ortega Hinojosa et al. ENVIRONMENTAL RESEARCH
- Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer
- (2014) Tsair-Fwu Lee et al. PLoS One
- Twitter: Big data opportunities--Response
- (2014) D. Lazer et al. SCIENCE
- Institute of Medicine. 2013. Evaluating Obesity Prevention Efforts: A Plan for Measuring Progress. Washington, DC: The National Academies Press, 2013
- (2014) Shelley McGuire Advances in Nutrition
- Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in the United States in Near Real-Time
- (2014) David J. McIver et al. PLoS Computational Biology
- A Primer on Predictive Models
- (2014) Akbar K Waljee et al. Clinical and Translational Gastroenterology
- Web-scale pharmacovigilance: listening to signals from the crowd
- (2013) Ryen W White et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- When Google got flu wrong
- (2013) Declan Butler NATURE
- Influenza Forecasting with Google Flu Trends
- (2013) Andrea Freyer Dugas et al. PLoS One
- Using Google Trends for Influenza Surveillance in South China
- (2013) Min Kang et al. PLoS One
- Google Flu Trends: Correlation With Emergency Department Influenza Rates and Crowding Metrics
- (2012) A. F. Dugas et al. CLINICAL INFECTIOUS DISEASES
- A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010
- (2012) Stephen S Lim et al. LANCET
- Search Query Data to Monitor Interest in Behavior Change: Application for Public Health
- (2012) Lucas J. Carr et al. PLoS One
- Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic
- (2011) Samantha Cook et al. PLoS One
- Implications of the Foresight Obesity System Map for Solutions to Childhood Obesity
- (2010) Diane T. Finegood et al. Obesity
- Predicting consumer behavior with Web search
- (2010) Sharad Goel et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Google Trends: A Web‐Based Tool for Real‐Time Surveillance of Disease Outbreaks
- (2009) Herman Anthony Carneiro et al. CLINICAL INFECTIOUS DISEASES
- The Public Health and Economic Benefits of Taxing Sugar-Sweetened Beverages
- (2009) Kelly D. Brownell et al. NEW ENGLAND JOURNAL OF MEDICINE
- Digital Disease Detection — Harnessing the Web for Public Health Surveillance
- (2009) John S. Brownstein et al. NEW ENGLAND JOURNAL OF MEDICINE
- Using Internet Searches for Influenza Surveillance
- (2008) Philip M. Polgreen et al. CLINICAL INFECTIOUS DISEASES
- Detecting influenza epidemics using search engine query data
- (2008) Jeremy Ginsberg et al. NATURE
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now