2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA) (2014)
Nov. 10, 2014 to Nov. 13, 2014
Ahmed Abdeen Hamed , Vermont EPSCoR, University of Vermont, Burlington, USA
Xindong Wu , Dept. of Computer Science, University of Vermont, Burlington, USA
Tamer Fandy , Dept. of Pharma Sciences, Albany College of Pharma, Colchester VT, USA
Can keyword-hashtag networks, derived from Big Data environments such as Twitter, yield clinicians a powerful tool to extrapolate patterns that may lead to development of new medical therapy and/or drugs? In our paper, we present a systematic network mining method to answer this question. We present HashnetMiner, a new pattern detection algorithm that operates on networks of medical concepts and hashtags. Concepts are selected from widely accessible databases (e.g., Medical Subject Heading [MeSH] descriptors, and Drugs.com), and hashtags are harvested from the associations with concepts that appear in tweets. The algorithm discerns promising discoveries that will be further explained in this paper. To the best of our knowledge, this is the first effort that uses Big Data networks mining to address such a question.
Twitter, Drugs, Association rules, Algorithm design and analysis, Big data, Terminology
A. A. Hamed, X. Wu and T. Fandy, "Mining patterns in Big Data K-H networks," 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA), Doha, Qatar, 2014, pp. 176-183.