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Real-time processing of social media with SENTINEL: A syndromic surveillance system incorporating deep learning for health classification

Ovidiu Șerban, Nicholas Thapen, Brendan Maginnis, Chris Hankin, Virginia Foot
2018 Information Processing & Management  
Interest in real-time syndromic surveillance based on social media data has greatly increased in recent years.  ...  It applies deep learning to the problem of classifying health-related tweets and is able to do so with high accuracy.  ...  Department of Defense's Defense Threat Reduction Agency (DTRA).  ... 
doi:10.1016/j.ipm.2018.04.011 fatcat:seorwli2ovd2bj5v4eoadkxcpa

Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance

Oduwa Edo-Osagie, Gillian Smith, Iain Lake, Obaghe Edeghere, Beatriz De La Iglesia, Olalekan Uthman
2019 PLoS ONE  
We investigate the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of  ...  Finally, we found some correlation (r = 0.414, p = 0.0004) between the Twitter signal generated with the semi-supervised system and data from consultations for related health conditions.  ...  Acknowledgments We acknowledge support from NHS 111 and NHS Digital for their assistance with the NHS 111 system; Out-of-Hours providers submitting data to the GPOOH syndromic surveillance and Advanced  ... 
doi:10.1371/journal.pone.0210689 pmid:31318885 pmcid:PMC6638773 fatcat:2kqtjnjrjvcdxcfqaur6765imu

Design Choices for Automated Disease Surveillance in the Social Web

Mark Abraham Magumba, Peter Nabende, Ernest Mwebaze
2018 Online Journal of Public Health Informatics  
We briefly discuss the technical rationale and practical implications of these different choices in addition to the key limitations associated with these systems within the context of operable disease  ...  The social web has emerged as a dominant information architecture accelerating technology innovation on an unprecedented scale.  ...  Indicator based surveillance encompasses traditional formal systems with regular, predetermined reporting times whereas event based surveillance is real-time and ad hoc and incorporates both formal and  ... 
doi:10.5210/ojphi.v10i2.9312 pmid:30349632 pmcid:PMC6194101 fatcat:l4zzy3yrqffftebpc4pcuq7szu

Artificial Intelligence (AI) in Evidence-Based Approaches to Effectively Respond to Public Health Emergencies [chapter]

Lap Yan Wong, Chun Kit Yip, Dao Shen Tan, Wai Lim Ling
2021 Evidence-Based Approaches to Effectively Respond to Public Health Emergencies [Working Title]  
A wide range of medical fields have used various well-developed deep learning algorithms.  ...  One of the examples of effectively reacting to public health emergencies is the need for developing AI evidence-based approaches to public health strategies for the scientific community's response to the  ...  Zheng Xiang from the University of Hong Kong for his support in the literature review. Conflict of interest The authors declare no conflict of interest. © 2021 The Author(s). Licensee IntechOpen.  ... 
doi:10.5772/intechopen.97499 fatcat:e7wsq7mu3bh2lbrly3wou77xkq

Epidemic Alert System: A Web-based Grassroots Model

Etinosa Noma Osaghae, Kennedy Okokpujie, Charles Ndujiuba, Olatunji Okesola, Imhade P. Okokpujie
2018 International Journal of Electrical and Computer Engineering (IJECE)  
Most web-based disease surveillance systems that give epidemic alerts are based on very large and unstructured data from various news sources, social media and online queries that are parsed by complex  ...  It makes use of a threshold value that is based on the third quartile (75th percentile) to determine the need to trigger the alarm for the onset of an epidemic.  ...  But emphasis today keeps shifting towards real-time disease surveillance [19] . The report submitted that the state-of-the-art for real-time disease surveillance depends heavily on social media.  ... 
doi:10.11591/ijece.v8i5.pp3809-3828 fatcat:bxrlv5ii5bd7pobkwxaroybdxa

Infectious Disease Research in the Era of Big Data

Peter M. Kasson
2020 Annual Review of Biomedical Data Science  
Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 3 is July 20, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.  ...  All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics.  ...  Despite the deployment of some functional systems, there remains a substantial gap between validation of retrospective analyses and the routine use of real-time automated syndromic surveillance from EHR  ... 
doi:10.1146/annurev-biodatasci-121219-025722 fatcat:nhaqugv5gna3xo2ipfp7hqyzky

Internet-Based Sources of Health Information: A Systematic Literature Review (Preprint)

Joana M Barros, James Duggan, Dietrich Rebholz-Schuhmann
2019 Journal of Medical Internet Research  
With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health.  ...  Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1% (154/162  ...  Acknowledgments This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289_P2, cofunded by the European Regional  ... 
doi:10.2196/13680 pmid:32167477 fatcat:jd2gymvy7zhqjdbmxjcadlbroi

Lessons from Ebola: Improving infectious disease surveillance to inform outbreak management

Mark E. J. Woolhouse, Andrew Rambaut, Paul Kellam
2015 Science Translational Medicine  
REAL-TIME SEQUENCE DATA Probably the most important addition to the arsenal of tools for outbreak investigation and guiding public health interventions is the production and use of time-resolved and geolocated  ...  Over the past decade, not only has a deep understanding and a detailed evolutionary framework been developed for, in particular, virus genetics (12), but powerful computational tools and high-throughput  ...  However, other new technologies, such as real-time sequencing and mathematical modeling, may be ready for integrating into surveillance systems.  ... 
doi:10.1126/scitranslmed.aab0191 pmid:26424572 pmcid:PMC5819730 fatcat:2zniguyqtnbyxccgbl3m7dfij4

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

Ania Syrowatka, Masha Kuznetsova, Ava Alsubai, Adam L. Beckman, Paul A. Bain, Kelly Jean Thomas Craig, Jianying Hu, Gretchen Purcell Jackson, Kyu Rhee, David W. Bates
2021 npj Digital Medicine  
Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations  ...  ; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment.  ...  Zoe Co for assistance with data abstraction. A.S. is supported by a Fellowship Award from the Canadian Institutes of Health Research.  ... 
doi:10.1038/s41746-021-00459-8 pmid:34112939 fatcat:itib6lu3cbdwvlwbkejpm3fusu

Can Social Media Data Be Utilized to Enhance Early Warning: Retrospective Analysis of the U.S. Covid-19 Pandemic [article]

Lingyao Li, Lei Gao, Jiayan Zhou, Zihui Ma, David Choy, Molly Hall
2021 medRxiv   pre-print
With the aid of natural language processing tools and machine learning classifiers, this study classifies each of these tweets into either a signal or a non-signal.  ...  This claim has been validated with a leading time of 16 days through the comparison to other referenced methods based on Google trends or media news.  ...  Most of the attempts combining social media data aim to provide real-time surveillance, which mainly relies on the classification of symptom-like messages.  ... 
doi:10.1101/2021.04.11.21255285 fatcat:jrmwn566tnesbiatpeofuf5moq

Predicting seasonal influenza using supermarket retail records [article]

Ioanna Miliou, Xinyue Xiong, Salvatore Rinzivillo, Qian Zhang, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi, Alessandro Vespignani
2020 arXiv   pre-print
The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.  ...  Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast  ...  IM has been partially supported by a "Grant for Young Mobility" (GYM 2018) of ISTI-CNR.  ... 
arXiv:2012.04651v2 fatcat:q4pmxpreonacvjvzdm3donmq4m

Artificial Intelligence Applications in Tracking Health Behaviors During Disease Epidemics [chapter]

Kurubaran Ganasegeran, Surajudeen Abiola Abdulrahman
2019 Learning and Analytics in Intelligent Systems  
From a systems-thinking perspective, and in today's world of seamless boundaries and global interconnectivity, AI offers enormous potential for public health practitioners and policy makers to revolutionize  ...  responsibility for their health and well-being.  ...  Acknowledgements We thank the Ministry of Health Malaysia for the support to publish this chapter.  ... 
doi:10.1007/978-3-030-35139-7_7 fatcat:kzc37kwhefetjp7jdco7iwxq64

Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples

Maged N Kamel Boulos, Bernd Resch, David N Crowley, John G Breslin, Gunho Sohn, Russ Burtner, William A Pike, Eduardo Jezierski, Kuo-Yu Chuang
2011 International Journal of Health Geographics  
of indoor and outdoor surveillance data in real-time and the development of next-generation, collaborative natural user interfaces that will power the spatially-enabled public health and emergency situation  ...  rooms of the future, where sensor data and citizen reports can be triaged and acted upon in real-time by distributed teams of professionals, this paper offers a comprehensive state-of-the-art review of  ...  DNC and JGB are funded in part by Science Foundation Ireland under grant number SFI/08/CE/I1380 (Lion 2) and by a CoEI, NUI Galway scholarship.  ... 
doi:10.1186/1476-072x-10-67 pmid:22188675 pmcid:PMC3271966 fatcat:zje67l7py5d6nejb2yhroxqvb4

Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America

José Antonio García-Díaz, Mar Cánovas-García, Rafael Valencia-García
2020 Future generations computer systems  
This is done by applying deep-learning models.  ...  Infodemiology is the process of mining unstructured and textual data so as to provide public health officials and policymakers with valuable information regarding public health.  ...  In addition, José Antonio García-Díaz has been supported by Banco Santander and University of Murcia through the Doctorado industrial programme.  ... 
doi:10.1016/j.future.2020.06.019 pmid:32572291 pmcid:PMC7301140 fatcat:xxt6mfojevf3zhpu4fchr3lzuq

An exploratory study of disease surveillance systems in Norway

Monika A Johansen, Jeremiah Scholl, Gudleif Aronsen, Gunnar Hartvigsen, Johan G Bellika
2008 Journal of Telemedicine and Telecare  
Acknowledgements First of all, I would like to thank Johan Gustav Bellika, who obtained funding for this PhD through the syndromic surveillance project, and the Northern Norway Regional Health Authority  ...  (Helse Nord RHF) for funding the PhD.  ...  a more timely syndromic surveillance system, or with using other epidemiological data under the diagnostic reasoning process.  ... 
doi:10.1258/jtt.2008.007010 pmid:18852319 fatcat:lwf7y2jx3be6tlfgurz56ge7ve
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