Evidence map›Paper›PMID 30671512›Full record

ArticleData in brief2019

PADI-web corpus: Labeled textual data in animal health domain.

Julien Rabatel, Elena Arsevska, Mathieu Roche

Abstract read
In one paragraph

Article in Data in brief, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Julien RabatelCirad, Montpellier, France.
Elena ArsevskaASTRE, Cirad, INRA, Montpellier, France.
Mathieu RocheTETIS, Univ. of Montpellier, AgroParisTech, Cirad, CNRS, Irstea, Montpellier, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monitoring animal health worldwide, especially the early detection of outbreaks of emerging pathogens, is one of the means of preventing the introduction of infectious diseases in countries (Collier et al., 2008) [3]. In this context, we developed PADI-web, a Platform for Automated extraction of animal Disease Information from the Web (Arsevska et al., 2016, 2018). PADI-web is a text-mining tool that automatically detects, categorizes and extracts disease outbreak information from Web news articles. PADI-web currently monitors the Web for five emerging animal infectious diseases, i.e., African swine fever, avian influenza including highly pathogenic and low pathogenic avian influenza, foot-and-mouth disease, bluetongue, and Schmallenberg virus infection. PADI-web collects Web news articles in near-real time through RSS feeds. Currently, PADI-web collects disease information from Google News because of its international and multiple language coverage. We implemented machine learning techniques to identify the relevant disease information in texts (i.e., location and date of an outbreak, affected hosts, their numbers and clinical signs). In order to train the model for Information Extraction (IE) from news articles, a corpus in English has been manually labeled by domain experts. This labeled corpus (Rabatel et al., 2017) is presented in this data paper.

Identifiers

PMID30671512
PMCPMC6327737

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.