Unsupervised Public Health Event Detection for Epidemic Intelligence

verfasst von

Marco Fisichella, Avaré Stewart, Kerstin Denecke, Wolfgang Nejdl

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.

Organisationseinheit(en)

Forschungszentrum L3S

Typ

Aufsatz in Konferenzband

Seiten

1881-1884

Anzahl der Seiten

4

Publikationsdatum

26.10.2010

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Allgemeine Entscheidungswissenschaften, Allgemeine Unternehmensführung und Buchhaltung

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1145/1871437.1871753 (Zugang: Unbekannt)