Roche Mathieu.
2023. How text-mining could improve surveillance systems?.
. Data4Earth Laboratory, Faculty of Sciences and Technics Beni Mellal, University Sultan Moulay Slimane
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Matériel d'accompagnement : 1 diaporama (64 vues)
Résumé : The ability to identify emerging and re-emerging diseases is challenging for the health domain. In this context, event-based surveillance (EBS) gathers information from heterogenous data sources, including online news articles. EBS systems integrate text-mining methods to deal with huge amounts of textual data. This talk focuses on the use text-mining and multidisciplinary approaches in order to mine news data dealing with the health domain. These data science approaches are integrated in an EBS system called PADI-web (Platform for Automated extraction of Disease Information from the web). PADI-web dedicated to animal health surveillance tackles disease-based and symptom-based surveillance. To address these issues different text-mining methods associated with labeled textual datasets are integrated in the main steps of EBS systems: data acquisition, information retrieval (i.e. identification of relevant texts), epidemiological information extraction, information to communicate to end-users. These methods are also adapted in other domain like Food security by mining heterogenous data.
Mots-clés libres : One Health, Event-based surveillance, Text Mining, Natural Language Processing, PADI-web
Agences de financement européennes : European Commission
Programme de financement européen : H2020
Projets sur financement : (EU) MOnitoring Outbreak events for Disease surveillance in a data science context
Auteurs et affiliations
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Roche Mathieu, CIRAD-ES-UMR TETIS (FRA)
ORCID: 0000-0003-3272-8568
Source : Cirad-Agritrop (https://agritrop.cirad.fr/604295/)
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