Conference proceeding
Eliciting Disease Data from Wikipedia Articles
Proceedings of the ... International AAAI Conference on Weblogs and Social Media, pp.26-33
05/2015
PMCID: PMC5511739
PMID: 28721308
Abstract
Traditional disease surveillance systems suffer from several disadvantages, including reporting lags and antiquated technology, that have caused a movement towards internet-based disease surveillance systems. Internet systems are particularly attractive for disease outbreaks because they can provide data in near real-time and can be verified by individuals around the globe. However, most existing systems have focused on disease monitoring and do not provide a data repository for policy makers or researchers. In order to fill this gap, we analyzed Wikipedia article content. We demonstrate how a named-entity recognizer can be trained to tag case counts, death counts, and hospitalization counts in the article narrative that achieves an F1 score of 0.753. We also show, using the 2014 West African Ebola virus disease epidemic article as a case study, that there are detailed time series data that are consistently updated that closely align with ground truth data. We argue that Wikipedia can be used to create the first community-driven open-source emerging disease detection, monitoring, and repository system.
Details
- Title: Subtitle
- Eliciting Disease Data from Wikipedia Articles
- Creators
- Geoffrey Fairchild - Los Alamos National LaboratorySara Y Del Valle - Los Alamos National LaboratoryLalindra De Silva - University of UtahAlberto M Segre - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the ... International AAAI Conference on Weblogs and Social Media, pp.26-33
- PMID
- 28721308
- PMCID
- PMC5511739
- ISSN
- 2162-3449
- eISSN
- 2334-0770
- Language
- English
- Date published
- 05/2015
- Academic Unit
- Nursing; Fraternal Order of Eagles Diabetes Research Center; Computer Science
- Record Identifier
- 9984259404402771
Metrics
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