Analyzing how people discuss about health-related topics on dedicated forums and social networks such as Twitter, can provide valuable insight for syndromic surveillance and to predict disease outbreaks. In this paper we present a minimally trained algorithm to learn associations between technical and everyday language terms, based on pattern generalization and complete linkage clustering, and we then assess its utility on a case study of five common syndromes for surveillance purposes.
CITATION STYLE
Stilo, G., De Vincenzi, M., Tozzi, A. E., & Velardi, P. (2013). Automated learning of everyday patients’ language for medical blogs analytics. In International Conference Recent Advances in Natural Language Processing, RANLP (pp. 640–648). Incoma Ltd.
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