TY - GEN
T1 - Real-Time disease surveillance using twitter data:Demonstration on flu and cancer
AU - Lee, Kathy
AU - Agrawal, Ankit
AU - Choudhary, Alok
N1 - Publisher Copyright:
Copyright © 2013 ACM.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2013/8/11
Y1 - 2013/8/11
N2 - Social media is producing massive amounts of data on an un- precedented scale. Here people share their experiences and opinions on various topics, including personal health issues, symptoms, treatments, side-effects, and so on. This makes publicly available social media data an invaluable resource for mining interesting and actionable healthcare insights. In this paper, we describe a novel real-Time u and cancer surveillance system that uses spatial, temporal, and text mining on Twitter data. The real-Time analysis results are reported visually in terms of US disease surveillance maps, distribution and timelines of disease types, symptoms, and treatments, in addition to overall disease activity timelines on our project website. Our surveillance system can be very useful not only for early prediction of seasonal disease out- breaks such as u, but also for monitoring distribution of cancer patients with different cancer types and symptoms in each state and the popularity of treatments used. The resulting insights are expected to help facilitate faster response to and preparation for epidemics and also be very useful for both patients and doctors to make more informed decisions.
AB - Social media is producing massive amounts of data on an un- precedented scale. Here people share their experiences and opinions on various topics, including personal health issues, symptoms, treatments, side-effects, and so on. This makes publicly available social media data an invaluable resource for mining interesting and actionable healthcare insights. In this paper, we describe a novel real-Time u and cancer surveillance system that uses spatial, temporal, and text mining on Twitter data. The real-Time analysis results are reported visually in terms of US disease surveillance maps, distribution and timelines of disease types, symptoms, and treatments, in addition to overall disease activity timelines on our project website. Our surveillance system can be very useful not only for early prediction of seasonal disease out- breaks such as u, but also for monitoring distribution of cancer patients with different cancer types and symptoms in each state and the popularity of treatments used. The resulting insights are expected to help facilitate faster response to and preparation for epidemics and also be very useful for both patients and doctors to make more informed decisions.
KW - Cancer
KW - Disease detection
KW - Disease surveillance
KW - Epidemics
KW - Inuenza
KW - Public health
KW - Social media
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85016706295&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016706295&partnerID=8YFLogxK
U2 - 10.1145/2487575.2487709
DO - 10.1145/2487575.2487709
M3 - Conference contribution
AN - SCOPUS:85016706295
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1474
EP - 1477
BT - KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
A2 - Parekh, Rajesh
A2 - He, Jingrui
A2 - Inderjit, Dhillon S.
A2 - Bradley, Paul
A2 - Koren, Yehuda
A2 - Ghani, Rayid
A2 - Senator, Ted E.
A2 - Grossman, Robert L.
A2 - Uthurusamy, Ramasamy
PB - Association for Computing Machinery
T2 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
Y2 - 11 August 2013 through 14 August 2013
ER -
