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Predicting antimicrobial drug consumption using web search data

Hansen, ND; Cox, IJ; Mølbak, K; Lioma, C; (2018) Predicting antimicrobial drug consumption using web search data. In: Kostkova, P and Grasso, F and Castillo, C and Mejova, Y and Bosman, A and Edelstein, M, (eds.) DH '18: Proceedings of the 2018 International Conference on Digital Health. (pp. pp. 133-142). Association for Computing Machinery (ACM): New York, USA. Green open access

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Consumption of antimicrobial drugs, such as antibiotics, is linked with antimicrobial resistance. Surveillance of antimicrobial drug consumption is therefore an important element in dealing with antimicrobial resistance. Many countries lack sufficient surveillance systems. Usage of web mined data therefore has the potential to improve current surveillance methods. To this end, we study how well antimicrobial drug consumption can be predicted based on web search queries, compared to historical purchase data of antimicrobial drugs. We present two prediction models (linear Elastic Net, and nonlinear Gaussian Processes), which we train and evaluate on almost 6 years of weekly antimicrobial drug consumption data from Denmark and web search data from Google Health Trends. We present a novel method of selecting web search queries by considering diseases and drugs linked to antimicrobials, as well as professional and layman descriptions of antimicrobial drugs, all of which we mine from the open web. We find that predictions based on web search data are marginally more erroneous but overall on a par with predictions based on purchases of antimicrobial drugs. This marginal difference corresponds to < 1% point mean absolute error in weekly usage. Best predictions are reported when combining both web search and purchase data. This study contributes a novel alternative solution to the real-life problem of predicting (and hence monitoring) antimicrobial drug consumption, which is particularly valuable in countries/states lacking centralised and timely surveillance systems.

Type: Proceedings paper
Title: Predicting antimicrobial drug consumption using web search data
Event: DH '18, 2018 International Conference on Digital Health, 23-26 April 2018, Lyon, France
ISBN-13: 9781450364935
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3194658.3194667
Publisher version: https://dx.doi.org/10.1145/3194658.3194667
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Web search query frequency, Prediction of antimicrobial drug use, Linear modelling, Gaussian Processes
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10059263
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