ReviewGlobal public health2012
Uncovering text mining: a survey of current work on web-based epidemic intelligence.
Review in Global public health, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Reconstructing historic and modern potato late blight outbreaks using text analytics.Scientific reports · 2024Article
- Artificial intelligence in public health: the potential of epidemic early warning systems.The Journal of international medical research · 2023Review
- Influenza and Measles-MMR: two case study of the trend and impact of vaccine-related Twitter posts in Spanish during 2015-2018.Human vaccines & immunotherapeutics · 2022Article
- The persistent threat of emerging plant disease pandemics to global food security.Proceedings of the National Academy of Sciences of the United States of America · 2021Review
- The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations.Child abuse & neglect · 2018Article
- Design Choices for Automated Disease Surveillance in the Social Web.Online journal of public health informatics · 2018Article
- Responsible Use of Pop Culture and Communication in the Face of Ebola Virus.PLoS neglected tropical diseases · 2015Article
- What makes people talk about antibiotics on social media? A retrospective analysis of Twitter use.The Journal of antimicrobial chemotherapy · 2014Article
- Big data in medicine is driving big changes.Yearbook of medical informatics · 2014Review
- Review
- Using open-source intelligence to identify early signals of COVID-19 in Indonesia.Western Pacific surveillance and response journal : WPSARArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Real world pandemics such as SARS 2002 as well as popular fiction like the movie Contagion graphically depict the health threat of a global pandemic and the key role of epidemic intelligence (EI). While EI relies heavily on established indicator sources a new class of methods based on event alerting from unstructured digital Internet media is rapidly becoming acknowledged within the public health community. At the heart of automated information gathering systems is a technology called text mining. My contribution here is to provide an overview of the role that text mining technology plays in detecting epidemics and to synthesise my existing research on the BioCaster project.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.