Autor(es)

Antonella Dellanzo and Viviana Cotik and Daniel Yunior Lozano Barriga and Jonathan Jimmy Mollapaza Apaza and Daniel Palomino and Fernando Schiaffino and Alexander Yanque Aliaga and José Ochoa-Luna

URL Pública

Abstract

In order to detect threats to public health and to be well-prepared for endemic and pandemic illness outbreaks, countries usually rely on event-based surveillance (EBS) and indicator-based surveillance systems. Event-based surveillance systems are key components of early warning systems and focus on fast capturing of data to detect threat signals through channels other than traditional surveillance. In this study, we develop Natural Language Processing tools that can be used within EBS systems. In particular, we focus on information extraction techniques that enable digital surveillance to monitor Internet data and social media.We created an annotated Spanish corpus from ProMED-mail health reports regarding disease outbreaks in Latin America. The corpus has been used to train algorithms for two information extraction tasks: named entity recognition and relation extraction. The algorithms …