Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 5th IEEE International Conference on Digital Ecosystems and Technologies, DEST 2011 |
| Seiten | 127-132 |
| Seitenumfang | 6 |
| Publikationsstatus | Veröffentlicht - 2 Nov. 2011 |
| Veranstaltung | 2011 5th IEEE International Conference on Digital Ecosystems and Technologies (DEST) - Daejeon, Südkorea Dauer: 31 Mai 2011 → 3 Juni 2011 |
Publikationsreihe
| Name | IEEE International Conference on Digital Ecosystems and Technologies |
|---|---|
| ISSN (Print) | 2150-4938 |
| ISSN (elektronisch) | 2150-4946 |
Abstract
The great influx of Medical-Web data makes the task of computer-assisted gathering and interpretation of Social Media-based Epidemic Intelligence (SM-EI) a very challenging one. State-of-the-art approaches usually use supervised machine learning algorithms to gather data from a variety of sources in this medical ecosystem, mining this data for specific event patterns and information discovery. Supervised approaches not only limit the type of detectable events, but also requires learning examples be given to the machine learning algorithm in advance. On the other hand, the more generic and flexible unsupervised machine learning methods currently produce such complex results, that the domain experts are not capable of assessing the results in a natural and efficient manner. In this paper, we present a novel framework with which SM-EI field practitioners can interact with medical ecosystem data, and assess the results of such complex unsupervised SM-EI algorithms. The assessment framework and the unsupervised epidemic event detection algorithm have been fully implemented and a quantitative study is presented to show the validity of the new approach to SM-EI.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computergrafik und computergestütztes Design
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Umweltwissenschaften (insg.)
- Environmental engineering
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Proceedings of the 5th IEEE International Conference on Digital Ecosystems and Technologies, DEST 2011. 2011. S. 127-132 5936610 (IEEE International Conference on Digital Ecosystems and Technologies).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - A User Study on Public Health Events Detected within the Medical Ecosystem
AU - Stewart, Avaré
AU - Herder, Eelco
AU - Smith, Matthew
AU - Nejdl, Wolfgang
N1 - Funding information: Acknowledgments. This work has been par tially funded by the European Union under the project Judicial Management by Digital Libraries Semantics (JUMAS FP7-214306) and by the Klaus Tschira Foundation, Heidelberg, Germany.
PY - 2011/11/2
Y1 - 2011/11/2
N2 - The great influx of Medical-Web data makes the task of computer-assisted gathering and interpretation of Social Media-based Epidemic Intelligence (SM-EI) a very challenging one. State-of-the-art approaches usually use supervised machine learning algorithms to gather data from a variety of sources in this medical ecosystem, mining this data for specific event patterns and information discovery. Supervised approaches not only limit the type of detectable events, but also requires learning examples be given to the machine learning algorithm in advance. On the other hand, the more generic and flexible unsupervised machine learning methods currently produce such complex results, that the domain experts are not capable of assessing the results in a natural and efficient manner. In this paper, we present a novel framework with which SM-EI field practitioners can interact with medical ecosystem data, and assess the results of such complex unsupervised SM-EI algorithms. The assessment framework and the unsupervised epidemic event detection algorithm have been fully implemented and a quantitative study is presented to show the validity of the new approach to SM-EI.
AB - The great influx of Medical-Web data makes the task of computer-assisted gathering and interpretation of Social Media-based Epidemic Intelligence (SM-EI) a very challenging one. State-of-the-art approaches usually use supervised machine learning algorithms to gather data from a variety of sources in this medical ecosystem, mining this data for specific event patterns and information discovery. Supervised approaches not only limit the type of detectable events, but also requires learning examples be given to the machine learning algorithm in advance. On the other hand, the more generic and flexible unsupervised machine learning methods currently produce such complex results, that the domain experts are not capable of assessing the results in a natural and efficient manner. In this paper, we present a novel framework with which SM-EI field practitioners can interact with medical ecosystem data, and assess the results of such complex unsupervised SM-EI algorithms. The assessment framework and the unsupervised epidemic event detection algorithm have been fully implemented and a quantitative study is presented to show the validity of the new approach to SM-EI.
UR - http://www.scopus.com/inward/record.url?scp=80055062098&partnerID=8YFLogxK
U2 - 10.1109/DEST.2011.5936610
DO - 10.1109/DEST.2011.5936610
M3 - Conference contribution
AN - SCOPUS:80055062098
SN - 9781457708725
T3 - IEEE International Conference on Digital Ecosystems and Technologies
SP - 127
EP - 132
BT - Proceedings of the 5th IEEE International Conference on Digital Ecosystems and Technologies, DEST 2011
T2 - 2011 5th IEEE International Conference on Digital Ecosystems and Technologies (DEST)
Y2 - 31 May 2011 through 3 June 2011
ER -