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

Meng, Z. et al. (2022) BioCaster in 2021: automatic disease outbreaks detection from global news media. Bioinformatics, 38(18), pp. 4446-4448. (doi: 10.1093/bioinformatics/btac497) (PMID:35900173) (PMCID:PMC9477518)

Abstract

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.

• Item Type: Articles
• Additional Information: The authors kindly acknowledges grant-in-aid support from the UK ESRC for project EPI-AI (ES/T012277/1).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Meng, Dr Zaiqiao
• Authors: Meng, Z., Okhmatovskaia, A., Polleri, M., Shen, Y., Powell, G., Fu, Z., Ganser, I., Zhang, M., King, N. B., Buckeridge, D., and Collier, N.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Bioinformatics
• Publisher: Oxford University Press
• ISSN: 1367-4803
• ISSN (Online): 1460-2059
• Published Online: 28 July 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Bioinformatics 38(18): 4446-4448
• Publisher Policy: Reproduced under a Creative Commons License