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BMC Bioinformatics. 2009 Nov 24;10:385. doi: 10.1186/1471-2105-10-385.

Automated vocabulary discovery for geo-parsing online epidemic intelligence.

Author information

1
Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, 300 Longwood Ave, Boston, MA 02115, USA. mikaela.keller@childrens.harvard.edu

Abstract

BACKGROUND:

Automated surveillance of the Internet provides a timely and sensitive method for alerting on global emerging infectious disease threats. HealthMap is part of a new generation of online systems designed to monitor and visualize, on a real-time basis, disease outbreak alerts as reported by online news media and public health sources. HealthMap is of specific interest for national and international public health organizations and international travelers. A particular task that makes such a surveillance useful is the automated discovery of the geographic references contained in the retrieved outbreak alerts. This task is sometimes referred to as "geo-parsing". A typical approach to geo-parsing would demand an expensive training corpus of alerts manually tagged by a human.

RESULTS:

Given that human readers perform this kind of task by using both their lexical and contextual knowledge, we developed an approach which relies on a relatively small expert-built gazetteer, thus limiting the need of human input, but focuses on learning the context in which geographic references appear. We show in a set of experiments, that this approach exhibits a substantial capacity to discover geographic locations outside of its initial lexicon.

CONCLUSION:

The results of this analysis provide a framework for future automated global surveillance efforts that reduce manual input and improve timeliness of reporting.

PMID:
19930702
PMCID:
PMC2787530
DOI:
10.1186/1471-2105-10-385
[Indexed for MEDLINE]
Free PMC Article

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