Ensemble learned vaccination uptake prediction using web search queries

Niels Dalum Hansen; Christina Lioma; Kåre Mølbak

doi:10.1145/2983323.2983882

Ensemble learned vaccination uptake prediction using web search queries

Niels Dalum Hansen, Christina Lioma, Kåre Mølbak

Department of Computer Science

6 Citations (Scopus)

1 Downloads (Pure)

Abstract

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	Undefined/Unknown
Title of host publication	Proceedings of the 25th ACM International Conference on Information and Knowledge Management
Number of pages	4
Publisher	IEEE
Publication date	24 Oct 2016
Pages	1953-1956
ISBN (Electronic)	978-1-4503-4073-1
DOIs	https://doi.org/10.1145/2983323.2983882
Publication status	Published - 24 Oct 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
Number	25
Country/Territory	United States
City	Indianapolis
Period	24/10/2016 → 28/10/2016

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/2983323.2983882

Hansen_2016_EnsembleFinal published version, 122 KB

Cite this

Hansen, ND, Lioma, C & Mølbak, K 2016, Ensemble learned vaccination uptake prediction using web search queries. in Proceedings of the 25th ACM International Conference on Information and Knowledge Management. IEEE, pp. 1953-1956, 25th ACM International Conference on Information and Knowledge Management, Indianapolis, United States, 24/10/2016. https://doi.org/10.1145/2983323.2983882

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