Postmarketing drug safety surveillance is important because many potential adverse drug reactions cannot be identified in the premarketing review process. It is reported that about 5% of hospital admissions are attributed to adverse drug reactions and many deaths are eventually caused, which is a serious concern in public health. Currently, drug safety detection relies heavily on voluntarily reporting system, electronic health records, or relevant databases. There is often a time delay before the reports are filed and only a small portion of adverse drug reactions experienced by health consumers are reported. Given the popularity of social media, many health social media sites are now available for health consumers to discuss any healthrelated issues, including adverse drug reactions they encounter. There is a large volume of health-consumercontributed content available, but little effort has been made to harness this information for postmarketing drug safety surveillance to supplement the traditional approach. In this work, we propose the association rule mining approach to identify the association between a drug and an adverse drug reaction. We use the alerts posted by Food and Drug Administration as the gold standard to evaluate the effectiveness of our approach. The result shows that the performance of harnessing health-related social media content to detect adverse drug reaction is good and promising.
Since adverse drug reactions (ADRs) represent a significant health problem all over the world, ADR detection has become an important research topic in drug safety surveillance. As many potential ADRs cannot be detected though premarketing review, drug safety currently depends heavily on postmarketing surveillance. Particularly, current postmarketing surveillance in the United States primarily relies on the FDA Adverse Event Reporting System (FAERS). However, the effectiveness of such spontaneous reporting systems for ADR detection is not as good as expected because of the extremely high underreporting ratio of ADRs. Moreover, it often takes the FDA years to complete the whole process of collecting reports, investigating cases, and releasing alerts. Given the prosperity of social media, many online health communities are publicly available for health consumers to share and discuss any healthcare experience such as ADRs they are suffering. Such health-consumer-contributed content is timely and informative, but this data source still remains untapped for postmarketing drug safety surveillance. In this study, we propose to use (1) association mining to identify the relations between a drug and an ADR and (2) temporal analysis to detect drug safety signals at the early stage. We collect data from MedHelp and use the FDA's alerts and information of drug labeling revision as the gold standard to evaluate the effectiveness of our approach. The experiment results show that healthrelated social media is a promising source for ADR detection, and our proposed techniques are effective to identify early ADR signals. ACM Reference Format:Haodong Yang and Christopher C. Yang. 2015. Using health-consumer-contributed data to detect adverse drug reactions by association mining with temporal analysis.
ObjectiveThis study explores the presence and actions of an electronic cigarette (e-cigarette) brand, Blu, on Twitter to observe how marketing messages are sent and diffused through the retweet (i.e., message forwarding) functionality. Retweet networks enable messages to reach additional Twitter users beyond the sender’s local network. We follow messages from their origin through multiple retweets to identify which messages have more reach, and the different users who are exposed.MethodsWe collected three months of publicly available data from Twitter. A combination of techniques in social network analysis and content analysis were applied to determine the various networks of users who are exposed to e-cigarette messages and how the retweet network can affect which messages spread.ResultsThe Blu retweet network expanded during the study period. Analysis of user profiles combined with network cluster analysis showed that messages of certain topics were only circulated within a community of e-cigarette supporters, while other topics spread further, reaching more general Twitter users who may not support or use e-cigarettes.ConclusionsRetweet networks can serve as proxy filters for marketing messages, as Twitter users decide which messages they will continue to diffuse among their followers. As certain e-cigarette messages extend beyond their point of origin, the audience being exposed expands beyond the e-cigarette community. Potential implications for health education campaigns include utilizing Twitter and targeting important gatekeepers or hubs that would maximize message diffusion.
Adverse drug reactions (ADRs) are causing a substantial amount of hospital admissions and deaths, which cannot be underestimated. Drug-drug interactions (DDIs) are an important patient safety problem and have been reported to cause a large portion of patient adverse events resulting in warning notices or the withdrawal of many drugs from the market. Currently, DDIs detection mainly depends on four kinds of data sources -clinical trial data, spontaneous reporting systems, electronic medical records, and chemical/pharmacologic databases, all of which have some limitations such as cohort biases, low reporting ratio, access issue, etc. In this study, we propose to detect DDIs signals from consumer contributed contents in online healthcare communities using associations mining. We conduct an experiment with thirteen drugs and three DDI associations. Leverage, lift and interaction ratio are used in the experiment. DrugBank is used as gold standard to test the performance of the approach. The results show that our techniques are promising to detect signals of DDIs and the proposed measure, interaction ratio, performs better than leverage and lift.
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