Details
| Original language | English |
|---|---|
| Title of host publication | CIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops |
| Pages | 1881-1884 |
| Number of pages | 4 |
| Publication status | Published - 26 Oct 2010 |
| Event | 19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10 - Toronto, ON, Canada Duration: 26 Oct 2010 → 30 Oct 2010 |
Publication series
| Name | International Conference on Information and Knowledge Management, Proceedings |
|---|
Abstract
Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. However, state-of-the-art systems for Epidemic Intelligence have not kept the pace with the growing need for more robust public health event detection. In this paper, we propose a game-changing approach where public health events are detected in an unsupervised manner. We address the problems associated with adapting an unsupervised learner to the medical domain and in doing so, propose an approach which combines aspects from different feature-based event detection methods. We evaluate our approach with a real world dataset with respect to the quality of article clusters. Our results show that we are able to achieve a precision of 66% and a recall of 81% when evaluated using manually annotated, real-world data. This shows promising results for the use of such techniques in this new problem setting.
Keywords
- Clustering, Epidemic intelligence, Retrospective medical event detection
ASJC Scopus subject areas
Sustainable Development Goals
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CIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops. 2010. p. 1881-1884 (International Conference on Information and Knowledge Management, Proceedings).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Unsupervised Public Health Event Detection for Epidemic Intelligence
AU - Fisichella, Marco
AU - Stewart, Avaré
AU - Denecke, Kerstin
AU - Nejdl, Wolfgang
PY - 2010/10/26
Y1 - 2010/10/26
N2 - Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. However, state-of-the-art systems for Epidemic Intelligence have not kept the pace with the growing need for more robust public health event detection. In this paper, we propose a game-changing approach where public health events are detected in an unsupervised manner. We address the problems associated with adapting an unsupervised learner to the medical domain and in doing so, propose an approach which combines aspects from different feature-based event detection methods. We evaluate our approach with a real world dataset with respect to the quality of article clusters. Our results show that we are able to achieve a precision of 66% and a recall of 81% when evaluated using manually annotated, real-world data. This shows promising results for the use of such techniques in this new problem setting.
AB - Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. However, state-of-the-art systems for Epidemic Intelligence have not kept the pace with the growing need for more robust public health event detection. In this paper, we propose a game-changing approach where public health events are detected in an unsupervised manner. We address the problems associated with adapting an unsupervised learner to the medical domain and in doing so, propose an approach which combines aspects from different feature-based event detection methods. We evaluate our approach with a real world dataset with respect to the quality of article clusters. Our results show that we are able to achieve a precision of 66% and a recall of 81% when evaluated using manually annotated, real-world data. This shows promising results for the use of such techniques in this new problem setting.
KW - Clustering
KW - Epidemic intelligence
KW - Retrospective medical event detection
UR - http://www.scopus.com/inward/record.url?scp=78651337570&partnerID=8YFLogxK
U2 - 10.1145/1871437.1871753
DO - 10.1145/1871437.1871753
M3 - Conference contribution
AN - SCOPUS:78651337570
SN - 9781450300995
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1881
EP - 1884
BT - CIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops
T2 - 19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10
Y2 - 26 October 2010 through 30 October 2010
ER -