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ToolsEdo-Osagie, Oduwa, De La Iglesia, Beatriz, Lake, Iain and Edeghere, Obaghe (2019) Deep Learning for Relevance Filtering in Syndromic Surveillance: A Case Study in Asthma/Difficulty Breathing. In: Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods ICPRAM. International Conference on Pattern Recognition Applications and Methods . Science and Technology Publications, Lda, CZE, pp. 491-500. ISBN 9789897583513
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Abstract
In this paper, we investigate deep learning methods that may extract some word context for Twitter mining for syndromic surveillance. Most of the work on syndromic surveillance has been done on the flu or Influenza- Like Illnesses (ILIs). For this reason, we decided to look at a different but equally important syndrome, asthma/difficulty breathing, as this is quite topical given global concerns about the impact of air pollution. We also compare deep learning algorithms for the purpose of filtering Tweets relevant to our syndrome of interest, asthma/difficulty breathing. We make our comparisons using different variants of the F-measure as our evaluation metric because they allow us to emphasise recall over precision, which is important in the context of syndromic surveillance so that we do not lose relevant Tweets in the classification. We then apply our relevance filtering systems based on deep learning algorithms, to the task of syndromic surveillance and compare the results with real-world syndromic surveillance data provided by Public Health England (PHE).We find that the RNN performs best at relevance filtering but can also be slower than other architectures which is important for consideration in real-time application. We also found that the correlation between Twitter and the real-world asthma syndromic surveillance data was positive and improved with the use of the deep- learning-powered relevance filtering. Finally, the deep learning methods enabled us to gather context and word similarity information which we can use to fine tune the vocabulary we employ to extract relevant Tweets in the first place.
| Item Type: | Book Section |
|---|---|
| Additional Information: | Funding Information: We acknowledge support from NHS 111 and NHS Digital for their assistance and support with the NHS 111 system; Out-of-Hours providers submitting data to the GPOOH syndromic surveillance and Advanced Heath & Care. The authors also acknowledge support from the Public Health England Real-time Syndromic Surveillance Team. Beatriz De La Iglesia and Iain Lake receive support from the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response. Publisher Copyright: © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. |
| Uncontrolled Keywords: | syndromic surveillance,machine learning,text classification,tweet classification,deep learning,artificial intelligence,computer vision and pattern recognition,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/1700/1702 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences Faculty of Science > School of Environmental Sciences |
| UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023) Faculty of Science > Research Groups > Data Science and AI University of East Anglia Schools > Faculty of Science > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research Faculty of Science > Research Groups > Environmental Social Sciences Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Science > Research Centres > Centre for Ecology, Evolution and Conservation |
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| Depositing User: | LivePure Connector |
| Date Deposited: | 30 Jan 2019 16:30 |
| Last Modified: | 16 Apr 2025 23:58 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/69759 |
| DOI: | 10.5220/0007366904910500 |
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