Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective
arXiv:1903.05801
Abstract
Epidemic intelligence deals with the detection of disease outbreaks using formal (such as hospital records) and informal sources (such as user-generated text on the web) of information. In this survey, we discuss approaches for epidemic intelligence that use textual datasets, referring to it as `text-based epidemic intelligence'. We view past work in terms of two broad categories: health mention classification (selecting relevant text from a large volume) and health event detection (predicting epidemic events from a collection of relevant text). The focus of our discussion is the underlying computational linguistic techniques in the two categories. The survey also provides details of the state-of-the-art in annotation techniques, resources and evaluation strategies for epidemic intelligence.
This paper is under review at ACM Computing Surveys. This version of the paper does not use the ACM Computing Surveys stylesheet. This arXiv version is to solicit feedback