Ensemble learned vaccination uptake prediction using web search queries

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-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 languageEnglish
Title of host publicationProceedings of the 25th ACM International Conference on Information and Knowledge Management
Number of pages4
PublisherIEEE
Publication date2016
Pages1953-1956
ISBN (Electronic)978-1-4503-4073-1
DOIs
Publication statusPublished - 2016
Event25th ACM International Conference on Information and Knowledge Management - Indianapolis, United States
Duration: 24 Oct 201628 Oct 2016
Conference number: 25

Conference

Conference25th ACM International Conference on Information and Knowledge Management
Nummer25
LandUnited States
ByIndianapolis
Periode24/10/201628/10/2016

ID: 167516168