EventEpi-A natural language processing framework for event-based surveillance.

PLoS Computational Biology
Auss AbboodS. Ghozzi

Abstract

According to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, including the WHO and the Robert Koch Institute (RKI), dedicated groups of public health agents sift through numerous articles and newsletters to detect relevant events. This media screening is one important part of event-based surveillance (EBS). Reading the articles, discussing their relevance, and putting key information into a database is a time-consuming process. To support EBS, but also to gain insights into what makes an article and the event it describes relevant, we developed a natural language processing framework for automated information extraction and relevance scoring. First, we scraped relevant sources for EBS as done at the RKI (WHO Disease Outbreak News and ProMED) and automatically extracted the articles' key data: disease, country, date, and confirmed-case count. For this, we performed named entity recognition in two steps: EpiTator, an open-source epidemiological annotation tool, suggested many different possibilities for each. We extracted the key country and disease using a heuristic with good results. We trained a naive Bayes classifier to find the key date a...Continue Reading

References

Jun 6, 2017·International Health·Malwina Carrion, Lawrence C Madoff

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Citations

Sep 4, 2021·Yearbook of Medical Informatics·Natalia GrabarUNKNOWN Section Editors of the IMIA Yearbook Section on Clinical Natural Language Processing

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Methods Mentioned

BETA
feature extraction

Software Mentioned

DeepL
Excel
GRITS
word2vec
INIG
EpiTator
NET
ASP
Global Rapid Identification Tool System ( GRITS )
learn

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