Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective
This is an incremental survey that synthesizes existing work for researchers in computational linguistics and public health informatics.
The survey reviews computational linguistic methods for detecting disease outbreaks from textual data, categorizing approaches into health mention classification and health event detection, and details current annotation techniques, resources, and evaluation strategies.
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.