  Due to globalization, geographic boundaries no longer serve as effective
shields for the spread of infectious diseases. In order to aid bio-surveillance
analysts in disease tracking, recent research has been devoted to developing
information retrieval and analysis methods utilizing the vast corpora of
publicly available documents on the internet. In this work, we present methods
for the automated retrieval and classification of documents related to active
public health events. We demonstrate classification performance on an
auto-generated corpus, using recurrent neural network, TF-IDF, and Naive Bayes
log count ratio document representations. By jointly modeling the title and
description of a document, we achieve 97% recall and 93.3% accuracy with our
best performing bio-surveillance event classification model: logistic
regression on the combined output from a pair of bidirectional recurrent neural
networks.

Information Retrieval;Machine Learning;Machine Learning