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 non-linear 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.
We have not identified any studies on prediction of national drug consumption using web search frequency data. This does not mean that drugs have not been included in the previous studies. Inspecting the list of queries used in the prediction models for ILI reveals that brand names for cough medicine, such as Tessalon @cite_17 , Tylenol @cite_20 or Robitussin @cite_22 , are included as predictors of ILI. This indicates that in the case of illness people query the web for information on the relevant medication @cite_24 , leading us to the hypothesis that drug consumption can be predicted based on web search query frequencies.
@cite_24 @cite_22 @cite_20 @cite_17
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Influenza-like illness (ILI) estimation from web search data is an important web analytics task. The basic idea is to use the frequencies of queries in web search logs that are correlated with past ILI activity as features when estimating current ILI activity. It has been noted that since influenza is seasonal, this approach can lead to spurious correlations with features queries that also exhibit seasonality, but have no relationship with ILI. Spurious correlations can, in turn, degrade performance. To address this issue, we propose modeling the seasonal variation in ILI activity and selecting queries that are correlated with the residual of the seasonal model and the observed ILI signal. Experimental results show that re-ranking queries obtained by Google Correlate based on their correlation with the residual strongly favours ILI-related queries. We present a machine learning-based methodology capable of providing real-time (“nowcast”) and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC’s ILI reports. We evaluate the predictive ability of our ensemble approach during the 2013–2014 (retrospective) and 2014–2015 (live) flu seasons for each of the four weekly time horizons. Our ensemble approach demonstrates several advantages: (1) our ensemble method’s predictions outperform every prediction using each data source independently, (2) our methodology can produce predictions one week ahead of GFT’s real-time estimates with comparable accuracy, and (3) our two and three week forecast estimates have comparable accuracy to real-time predictions using an autoregressive model. Moreover, our results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizons. This report introduces a computational model based on internet search queries for real-time surveillance of influenza-like illness (ILI), which reproduces the patterns observed in ILI data from the Centers for Disease Control and Prevention. Drugs provide help and promise for human health, but they usually come with side effects. Predicting side effects of drugs is a critical issue for the drug discovery. Although several machine-learning methods were proposed to predict the drug side effects, it remains the space for the improvement. To the best of our knowledge, many side effects are not detectable in clinical trials until drugs are approved, thus predicting potential or missing side effects based on the known side effects is important for the postmarketing surveillance. In order to solve this specific problem, we formulate approved drugs, side effect terms and drug-side effect associations as a recommender system, and transform the problem of predicting side effects into a recommender task. Two recommender methods, i.e. the integrated neighborhood-based method and the restricted Boltzmann machine-based method, are designed to make predictions. Further, in order to achieve better performances, we combine proposed methods and existing methods of the same type to develop ensemble models. Compared with benchmark methods, the proposed methods and the ensemble method lead to better performances, and the statistical analysis demonstrates the improvements are significant (p-value<0.05). In conclusion, the integrated neighborhood-based method, the restricted Boltzmann machine-based method and the ensemble method are promising tools for the side effect prediction. The source codes and datasets are provided as the supplementary. Predicting side effects of drugs is a critical issue for the drug discovery.We transform approved drugs, side effect terms and drug-side effect associations as a recommender system.We design two recommender methods, i.e. the integrated neighborhood-based method and the restricted Boltzmann machine-based method, to make predictions.Further, we combine proposed methods and existing methods of the same type to develop ensemble models.