Unified approach to retrospective event detection for event- based epidemic intelligence

authored by

Marco Fisichella

Abstract

Inferring the magnitude and occurrence of real-world events from natural language text is a crucial task in various domains. Particularly in the domain of public health, the state-of-the-art document and token centric event detection approaches have not kept the pace with the growing need for more robust event detection in public health. In this paper, we propose UPHED, a unified approach, which combines both the document and token centric event detection techniques in an unsupervised manner such that events which are: rare (aperiodic); reoccurring (periodic) can be detected using a generative model for the domain of public health. We evaluate the efficiency of our approach as well as its effectiveness for two real-world case studies with respect to the quality of document clusters. Our results show that we are able to achieve a precision of 60% and a recall of 71% analyzed using manually annotated real-world data. Finally, we also make a comparative analysis of our work with the well-established rule-based system of MedISys and find that UPHED can be used in a cooperative way with MedISys to not only detect similar anomalies, but can also deliver more information about the specific outbreak of reported diseases.

Organisation(s)

L3S Research Centre

Type

Article

Journal

International Journal on Digital Libraries

Volume

22

Pages

339-364

No. of pages

26

ISSN

1432-5012

Publication date

12.2021

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Library and Information Sciences

Sustainable Development Goals

SDG 3 - Good Health and Well-being

Electronic version(s)

https://doi.org/10.1007/s00799-021-00308-9 (Access: Open)