TY  - GEN
PB  - Association for Computing Machinery (ACM)
N2  - 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.
UR  - https://dx.doi.org/10.1145/3194658.3194667
Y1  - 2018/04/23/
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
TI  - Predicting antimicrobial drug consumption using web search data
AV  - public
ID  - discovery10059263
EP  - 142
T3  - ACM International Conference Proceeding Series
KW  - Web search query frequency
KW  -  Prediction of antimicrobial drug use
KW  -  Linear modelling
KW  -  Gaussian Processes
CY  - New York, USA
A1  - Hansen, ND
A1  - Cox, IJ
A1  - Mølbak, K
A1  - Lioma, C
SP  - 133
ER  -