ArticleDecision support systems2009
Automatic online news monitoring and classification for syndromic surveillance.
Article in Decision support systems, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Elaboration of a new framework for fine-grained epidemiological annotation.Scientific data · 2022Article
- A Complete Process of Text Classification System Using State-of-the-Art NLP Models.Computational intelligence and neuroscience · 2022Review
- PADI-web 3.0: A new framework for extracting and disseminating fine-grained information from the news for animal disease surveillance.One health (Amsterdam, Netherlands) · 2021Article
- Assessment of the opportunities for increasing the availability of EU data on consumer product-related injuries.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2021Article
- Automated Classification of Online Sources for Infectious Disease Occurrences Using Machine-Learning-Based Natural Language Processing Approaches.International journal of environmental research and public health · 2020Article
- Article
- 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
- The value of necropsy reports for animal health surveillance.BMC veterinary research · 2018Article
- Web monitoring of emerging animal infectious diseases integrated in the French Animal Health Epidemic Intelligence System.PloS one · 2018Article
- TOY SAFETY SURVEILLANCE FROM ONLINE REVIEWS.Decision support systems · 2016Article
- International society for disease surveillance conference 2011: building the future of public health surveillance.Emerging health threats journal · 2011Article
- Redefining syndromic surveillance.Journal of epidemiology and global health · 2011Review
- An exploratory study of a text classification framework for Internet-based surveillance of emerging epidemics.International journal of medical informatics · 2011Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Syndromic surveillance can play an important role in protecting the public's health against infectious diseases. Infectious disease outbreaks can have a devastating effect on society as well as the economy, and global awareness is therefore critical to protecting against major outbreaks. By monitoring online news sources and developing an accurate news classification system for syndromic surveillance, public health personnel can be apprised of outbreaks and potential outbreak situations. In this study, we have developed a framework for automatic online news monitoring and classification for syndromic surveillance. The framework is unique and none of the techniques adopted in this study have been previously used in the context of syndromic surveillance on infectious diseases. In recent classification experiments, we compared the performance of different feature subsets on different machine learning algorithms. The results showed that the combined feature subsets including Bag of Words, Noun Phrases, and Named Entities features outperformed the Bag of Words feature subsets. Furthermore, feature selection improved the performance of feature subsets in online news classification. The highest classification performance was achieved when using SVM upon the selected combination feature subset.
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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.