Analyzing Adverse Event Signal Detection with Publicly Available Web Sources

Data mining for drug-reaction associations is a major topic in the pharmaceutical industry. Historically the focus has been on using privately owned and maintained datasets consisting of information available in the FDA Adverse Event Reporting System (FAERS) and privatized reporting systems that house the data from clinical trials. Our focus will be on building a pipeline that demonstrates an open source solution for outlining a drug's safety profile from data collection through signal detection. This pipeline primarily uses data from the openFDA and the social media site Reddit, both of which provide a well-documented API. All data analysis for this pipeline is done in the R statistical programming language. The aim was to collect the information available in these public sources and apply popular data mining methodologies used to identify and predict the occurrence of adverse events. The results show the ability of the openFDA and social media sites to create real-time drug safety profiles by applying the same statistical methods applied in clinical trials. The social media data does not perform equally across all drug types and gives best results when applied to common over-the-counter drugs as opposed to last line of defense medications.