M-Eco: Medical Ecosystem-Personalized Event-based Surveillance

Czech title

Medical Ecosystem-Personalized Event-based Surveillance

Type

grant

Keywords

Public Health Event Detection, Personalization, User
Generated Content

Abstract

Many factors in today’s changing societies contribute towards the continuous emergence of infectious diseases. Demographic change, globalization, bioterrorism, compounded with the resilient nature of viruses and diseases such as SARS and avian influenza have raised awareness for European society’s increasing vulnerability. Traditional Epidemic Intelligence systems are designed to identify potential health threats, and rely upon data transmissions from laboratories or hospitals. They can be used to recognise long-term trends, but are limited in several ways. Threats, such as SARS, can go unrecognised since the signals indicating its existence may originate from sources other than the traditional ones. Second, a critical strategy for circumventing devastating public health events is early detection and early response. Conflictingly, the time with which information propagates through the traditional channels, can undermine time-sensitive strategies. Finally, traditional systems are well suited for recognising indicators for known diseases, but are not well designed for detecting those that are emerging. Faced with these limitations, traditional systems need to be complemented with additional approaches which are better targeted for the early detection of emerging threats. The Medical EcoSystem (M-Eco) project, will address these limitations by using Open Access Media and User Generated Content, as unofficial information sources for Epidemic Intelligence. This type of content has transformed the manner in which information propagates across the globe. Based on this, M-Eco will develop an Event-Based Epidemic Intelligence System which integrates unofficial and traditional sources for the early detection of emerging health threats. M-Eco will emphasize adaptivity and personalized filtering so that relevant signals can be detected for targeting the needs of public health officials who have to synthesize facts, assess risks and react to public health threats.

Team members

Smrž Pavel, doc. RNDr., Ph.D. (UPGM FIT VUT) , research leader
Otrusina Lubomír, Ing. (FIT VUT) , team leader
Zemčík Pavel, prof. Dr. Ing. (UPGM FIT VUT) , team leader

Publications

2013

DENECKE Kerstin, KRIECK Manuela, OTRUSINA Lubomír, SMRŽ Pavel, DOLOG Peter, NEJDL Wolfgang and VELASCO Edward. How to Exploit Twitter for Public Health Monitoring. Methods of Information in Medicine, vol. 52, no. 4, 2013, pp. 326-339. ISSN 0026-1270. Detail

2012

BACKFRIED Gerhard, OTRUSINA Lubomír and SMRŽ Pavel. M-Eco D3.3 - M-Eco Media Content Analysis. Hannover: The Information Society Technologies (IST) 7th Framework programme, 2012. Detail
DENECKE Kerstin, DOLOG Peter and SMRŽ Pavel. Making use of social media data in public health. In: Proceedings of the 21st international conference companion on World Wide Web. New York: Association for Computing Machinery, 2012, pp. 243-246. ISBN 978-1-4503-1230-1. Detail

2011

DENECKE Kerstin, DREESMAN Johannes, KRIECK Manuela and OTRUSINA Lubomír. A New Age of Public Health: Identifying Disease Outbreaks by Analyzing Tweets. In: Proceedings of Health WebScience Workshop, ACM Web Science Conference. Koblenz: Association for Computing Machinery, 2011, pp. 10-15. Detail
DENECKE Kerstin, KIRCHNER Göran, DOLOG Peter, SMRŽ Pavel, LINGE Jens, BACKFRIED Gerhard and DREESMAN Johannes. Event-Driven Architecture for Health Event Detection from Multiple Sources. In: Proceedings of the XXIII International Conference of the European Federation for Medical Informatics (MIE 2011). Oslo: IOS Press, 2011, pp. 160-164. ISBN 978-1-60750-805-2. Detail
OTRUSINA Lubomír and SMRŽ Pavel. Finding Indicators of Epidemiological Events by Analyzing Messages from Twitter and Other Social Media. In: 20th ACM Conference on Information and Knowledge Management workshop proceedings by ACM. Glasgow: Association for Computing Machinery, 2011, p. 4. ISBN 978-1-4503-0950-9. Detail
OTRUSINA Lubomír and SMRŽ Pavel. M-Eco D3.2 - Semantic Annotator. Hannover: The Information Society Technologies (IST) 7th Framework programme, 2011. Detail
DREESMAN Johannes, ECKMANNS Tim, KRIECK Manuela, LINGE Jens, OTRUSINA Lubomír and VELASCO Edward. Norovirus outbreak at an university in Lower Saxony. In: Proceedings of the International Meeting on Emerging Diseases and Surveillance (IMED 2011). Brookline: International Society for Infectious Diseases, 2011, pp. 1-9. Detail
DREESMAN Johannes, ECKMANNS Tim, KRIECK Manuela, LINGE Jens, OTRUSINA Lubomír and VELASCO Edward. Norovirus outbreak at an university in Lower Saxony. In: Proceedings of the European Congress of Clinical Microbiology and Infectious Diseases (ECCMID 2011). Milan: The European Society of Clinical Microbiology and Infectious Diseases, 2011, pp. 1-9. Detail

2010

OTRUSINA Lubomír and SMRŽ Pavel. A New Approach to Pseudoword Generation. In: Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10). Valletta: European Language Resources Association, 2010, pp. 1-5. ISBN 2-9517408-6-7. Detail
BACKFRIED Gerhard, OTRUSINA Lubomír and SMRŽ Pavel. M-Eco D3.1 - Speech Recognition and Content Classification Subsystems. Hannover: Information and Communication Technologies (ICT) 7th Framework programme, 2010. Detail

Products

2012

Content collector and document analysis for the M-Eco project, software, 2012
Authors: Jeřábek Jan, Marek Tomáš, Otrusina Lubomír, Rylko Vojtěch, Smrž Pavel, Sznapka Jakub, Šafář Martin, Uherčík Maroš Detail

2011

BUT Recognizer of GeoNames, software, 2011
Authors: Otrusina Lubomír, Smrž Pavel, Sznapka Jakub, Šafář Martin Detail

2010

Analysis of data from social networks, software, 2010
Authors: Otrusina Lubomír, Smrž Pavel Detail