Ensemble learned vaccination uptake prediction using web search queries
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Ensemble learned vaccination uptake prediction using web search queries. / Hansen, Niels Dalum; Lioma, Christina; Mølbak, Kåre.
Proceedings of the 25th ACM International Conference on Information and Knowledge Management. IEEE, 2016. p. 1953-1956.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Ensemble learned vaccination uptake prediction using web search queries
AU - Hansen, Niels Dalum
AU - Lioma, Christina
AU - Mølbak, Kåre
N1 - Conference code: 25
PY - 2016
Y1 - 2016
N2 - 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).
AB - 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).
KW - cs.IR
KW - stat.AP
U2 - 10.1145/2983323.2983882
DO - 10.1145/2983323.2983882
M3 - Article in proceedings
SP - 1953
EP - 1956
BT - Proceedings of the 25th ACM International Conference on Information and Knowledge Management
PB - IEEE
Y2 - 24 October 2016 through 28 October 2016
ER -
ID: 167516168