2014.ISDS.Abstracts.Final.pdf


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ISDS 2014 Conference Abstracts

Standardising Syndromic Classification in Animal Health 
Data
Fernanda C. Dórea*1, Céline Dupuy2, Flavie Vial3, Crawford Revie4 and Ann Lindberg1

1Department of Epidemiology and Disease Control, National Veterinary Institute, Uppsala, Sweden; 2French Agency for Food, 
Environmental and Occupational Health & Safety (ANSES), Lyon, France; 3Veterinary Public Health Institute, Bern, Switzerland; 
4Centre for Veterinary Epidemiological Research, Charlottetown, PE, Canada

Objective
To develop an ontology for the classification of animal health data 

into syndromes with application to syndromic surveillance.

Introduction
Veterinary syndromic surveillance (VSS) is a fast growing field, 

but development has been limited by the limited use of standards 
in recording animal health events and thus their categorization into 
syndromes. The adoption of syndromic classification standards would 
allow comparability of outputs from systems using a variety of animal 
health data sources (clinical data, laboratory tests, slaughterhouse 
records, rendering plants data, etc), in addition to improving the 
ability to compare outputs among countries.

The project “Standardising Syndromic Classification in Animal 
Health Data” (SSynCAHD) aims to standardize the classification of 
animal health records into syndromes.

Methods
SSynCAHD will make use of current technologies from 

information management, which aim to promote intelligent access to 
data. In particular, SSynCAHD will be informed by the latest thinking 
relating to the Semantic Web, which supports the development of 
frameworks to maximize the potential for data sharing and reuse.

The use of an ontology allows the knowledge base used for 
syndromic classification to be structured into a formal representation. 
Formally, “an ontology defines a common vocabulary for researchers 
who need to share information in a domain. It includes machine-
interpretable definitions of basic concepts in the domain and relations 
among them” [1].

The use of ontologies in human syndromic surveillance has been 
previously explored by Conway and collaborators [2]. In animal 
health surveillance, further challenges are posed by the number of 
species monitored, the variety of data sources explored, and the lack 
of homogeneity in the syndromes defined in different systems.

In order to investigate the potential for developing an ontology 
for animal health syndromic classification, a workshop was held in 
May 2014, gathering interested experts with experience in veterinary 
syndromic surveillance and/or animal health data recording.

Results
The results of the workshop indicated strong interest in the field. 

It was proposed that development should be based on one syndrome 
at a time, and be data-driven; that is, collaborators would grow the 
ontology, for each syndrome, based on an analysis of the data sources 
available to them. The syndromes “reproductive” and “respiratory” 
were chosen as a starting point. The data-driven development will be 
intercalated with rounds of expert elicitation. Workshops and digital 
elicitation will serve to resolve conflicts, take decisions when needed, 
and ensure completeness of the concepts included in the ontology. The 
project will also promote the use of existing controlled vocabularies 
and classification standards in animal health. A follow-up workshop 

is planned for March 2015 in connection with the annual conference 
of the Society for Veterinary Epidemiology and Preventive Medicine.

The development work to be carried out prior to the 2015 workshop 
will consist of: an inventory of veterinary surveillance systems and 
the syndrome definitions used; an inventory of the use of ontologies 
in health surveillance; and development of a template of collaboration 
for the data-driven steps in ontology construction.

Conclusions
SSynCAHD proposes to harmonise syndromic surveillance data 

use rather than data recording. This will be achieved by standardising 
the classification of records into syndromes. Potential advantages 
include: an ability to achieve syndromic classification from different 
sources of data which are recorded using an institution’s own 
vocabulary; the ability to compare VSS system outputs within and 
between countries; and the more timely development of VSS systems.

Keywords
Animal health; syndromic surveillance; ontology

References

[1] Ontology Development 101: A Guide to Creating Your First Ontology 
2001. Available at http://protege.stanford.edu/publications/ontology_
development/ontology101.pdf.

[2] Conway M, Dowling J, Chapman W. Developing an application 
ontology for mining free text clinical reports: the extended syndromic 
surveillance ontology. In: 3rd international workshop on health 
document text mining and information analysis (LOUHI 2011); 2011. 
p. 75–82.

*Fernanda C. Dórea
E-mail: fernanda.dorea@sva.se    

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * (1):e123, 201