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Standardising Syndromic Classification in Animal Health Data
Author(s) -
Fernanda C. Dórea,
Céline Dupuy,
Flavie Vial,
Crawford W. Revie,
Ann Lindberg
Publication year - 2015
Publication title -
online journal of public health informatics
Language(s) - English
Resource type - Journals
ISSN - 1947-2579
DOI - 10.5210/ojphi.v7i1.5789
Subject(s) - computer science , data sharing , vocabulary , data science , health records , ontology , controlled vocabulary , data mining , information retrieval , medicine , health care , pathology , alternative medicine , political science , philosophy , linguistics , epistemology , law
Data sharing remains a barrier to joint surveillance and the establishment of contingency plans among countries and institutions. Summary statistics are hard to interpret and compare, and nomenclatures for animal disease classification are seldom used. SSynCAHD (Syndromic Classification in Animal Health Data) proposes to harmonise, through the development on an ontology, syndromic surveillance data use rather than data recording. This will be achieved by standardising classification into syndromes, based on records from different sources of animal health data which are (and will continue to be) recorded using an institution own vocabulary.

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