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  1. Proceedings of the International Workshop on Software Engineering in Healthcare Systems (SEHS '16)
  2. Mining Twitter data for influenza detection and surveillance
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Aligning healthcare innovation and software requirements through design thinking
Closing the gap: enacting knowledge transfer between engineering and use of healthcare software
Connected health: from rural Ireland to rural India
Smart checklists to improve healthcare outcomes
Coordinating analytics methods for mobile healthcare applications
Specification and analysis of human-intensive system resource-utilization policies
Patient-centric healthcare service systems: evidence-based medicine as architecturally significant requirement
Mining Twitter data for influenza detection and surveillance
A language based model for analysis of communication intensive processes in health care
An empirical investigation of the evaluators' scoring of vendors' responses to an RFP of a large healthcare system
Healthcare systems quality: development and use

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Mining Twitter data for influenza detection and surveillance

Content Provider ACM Digital Library
Author Byrd, Kenny Baysal, Olga Mansurov, Alisher
Abstract Twitter --- a social media platform --- has gained phenomenal popularity among researchers who have explored its massive volumes of data to offer meaningful insights into many aspects of modern life. Twitter has also drawn great interest from public health community to answer many health-related questions regarding the detection and spread of certain diseases. However, despite the growing popularity of Twitter as an influenza detection source among researchers, healthcare officials do not seem to be as intrigued by the opportunities that social media offers for detecting and monitoring diseases. In this paper, we demonstrate that 1) Twitter messages (tweets) can be reliably classified based on influenza related keywords; 2) the spread of influenza can be predicted with high accuracy; and, 3) there is a way to monitor the spread of influenza in selected cities in real-time. We propose an approach to efficiently mine and extract data from Twitter streams, reliably classify tweets based on their sentiment, and visualize data via a real-time interactive map. Our study benefits not only aspiring researchers who are interested in conducting a study involving the analysis of Twitter data but also health sectors officials who are encouraged to incorporate the analysis of vast information from social media data sources, in particular, Twitter.
Starting Page 43
Ending Page 49
Page Count 7
File Format PDF
ISBN 9781450341684
DOI 10.1145/2897683.2897693
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-05-14
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Sentiment analysis Cold symptoms Public health surveillance Social media Data mining Flu Visualization tool
Content Type Text
Resource Type Article
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