Twitter + 医療

Towards detecting influenza epidemics by analyzing Twitter messages
http://snap.stanford.edu/soma2010/papers/soma2010_16.pdf

Rapid response to a health epidemic is critical to reduce loss
of life. Existing methods mostly rely on expensive surveys
of hospitals across the country, typically with lag times of
one to two weeks for influenza reporting, and even longer
for less common diseases. In response, there have been
several recently proposed solutions to estimate a population’s health from Internet activity, most notably Google’s
Flu Trends service, which correlates search term frequency
with influenza statistics reported by the Centers for Disease
Control and Prevention (CDC). In this paper, we analyze
messages posted on the micro-blogging site Twitter.com to
determine if a similar correlation can be uncovered. We
propose several methods to identify influenza-related messages and compare a number of regression models to correlate these messages with CDC statistics. Using over 500,000
messages spanning 10 weeks, we find that our best model
achieves a correlation of .78 with CDC statistics by leveraging a document classifier to identify relevant messages.

Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter

http://www.aclweb.org/anthology/D/D11/D11-1145.pdf

With the recent rise in popularity and  scale 
of social media, a growing need exists for 
systems  that can extract useful information 
from huge amounts of data. We address the 
issue  of  detecting  influenza  epidemics. 
First, the proposed system extracts influenza related tweets using Twitter API. Then, 
only tweets that mention actual influenza 
patients are extracted by the support vector 
machine (SVM) based classifier. The experiment results demonstrate the feasibility 
of the proposed approach (0.89 correlation 
to the gold standard). Especially at the outbreak and early spread (early epidemic 
stage), the proposed method shows high 
correlation  (0.97 correlation), which  outperforms the  state-of-the-art methods. This 
paper describes that Twitter texts reflect 
the real world, and that NLP techniques 
can  be applied to extract only  tweets that 
contain useful information.

TWITTER IMPROVES SEASONAL INFLUENZA PREDICTION

http://www.cs.uml.edu/~hachreka/SNEFT/images/healthinf_2012.pdf