Ensemble learned vaccination uptake prediction using web search queries
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
We present a method that uses ensemble learning to combine clinical and web-mined time-series data in order to predict future vaccination uptake. The clinical data is official vaccination registries, and the web data is query frequencies collected from Google Trends. Experiments with official vaccine records show that our method predicts vaccination uptake eff?ectively (4.7 Root Mean Squared Error). Whereas performance is best when combining clinical and web data, using solely web data yields comparative performance. To our knowledge, this is the ?first study to predict vaccination uptake using web data (with and without clinical data).
Original language | English |
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Title of host publication | Proceedings of the 25th ACM International Conference on Information and Knowledge Management |
Number of pages | 4 |
Publisher | IEEE |
Publication date | 2016 |
Pages | 1953-1956 |
ISBN (Electronic) | 978-1-4503-4073-1 |
DOIs | |
Publication status | Published - 2016 |
Event | 25th ACM International Conference on Information and Knowledge Management - Indianapolis, United States Duration: 24 Oct 2016 → 28 Oct 2016 Conference number: 25 |
Conference
Conference | 25th ACM International Conference on Information and Knowledge Management |
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Nummer | 25 |
Land | United States |
By | Indianapolis |
Periode | 24/10/2016 → 28/10/2016 |
ID: 167516168