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BioCaster in 2021: automatic disease outbreaks detection from global news media.

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Okhmatovskaia, Anya 
Polleri, Maxime 
Shen, Yannan 
Powell, Guido 

Abstract

SUMMARY: BioCaster was launched in 2008 to provide an ontology-based text mining system for early disease detection from open news sources. Following a 6-year break, we have re-launched the system in 2021. Our goal is to systematically upgrade the methodology using state-of-the-art neural network language models, whilst retaining the original benefits that the system provided in terms of logical reasoning and automated early detection of infectious disease outbreaks. Here, we present recent extensions such as neural machine translation in 10 languages, neural classification of disease outbreak reports and a new cloud-based visualization dashboard. Furthermore, we discuss our vision for further improvements, including combining risk assessment with event semantics and assessing the risk of outbreaks with multi-granularity. We hope that these efforts will benefit the global public health community. AVAILABILITY AND IMPLEMENTATION: BioCaster web-portal is freely accessible at http://biocaster.org.

Description

Keywords

Population Surveillance, Disease Outbreaks, Data Mining, Semantics

Journal Title

Bioinformatics

Conference Name

Journal ISSN

1367-4803
1367-4811

Volume Title

Publisher

Oxford University Press (OUP)
Sponsorship
ESRC (ES/T012277/1)
ESRC grant ES/T012277/1