Ensemble learned vaccination uptake prediction using web search queries

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

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

Harvard

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

APA

Hansen, N. D., 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 (pp. 1953-1956). IEEE. https://doi.org/10.1145/2983323.2983882

Vancouver

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

Author

Hansen, Niels Dalum ; Lioma, Christina ; Mølbak, Kåre. / Ensemble learned vaccination uptake prediction using web search queries. Proceedings of the 25th ACM International Conference on Information and Knowledge Management. IEEE, 2016. pp. 1953-1956

Bibtex

@inproceedings{406ebc68080e4ca6851d20a4c715d7cf,
title = "Ensemble learned vaccination uptake prediction using web search queries",
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).",
keywords = "cs.IR, stat.AP",
author = "Hansen, {Niels Dalum} and Christina Lioma and K{\aa}re M{\o}lbak",
year = "2016",
doi = "10.1145/2983323.2983882",
language = "English",
pages = "1953--1956",
booktitle = "Proceedings of the 25th ACM International Conference on Information and Knowledge Management",
publisher = "IEEE",
note = "null ; Conference date: 24-10-2016 Through 28-10-2016",

}

RIS

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