Xiao Qin (Worcester Polytechnic Institute & Oak Ridge Institute of Science);Tabassum Kakar (Worcester Polytechnic Institute & Oak Ridge Institute of Science);Susmitha Wunnava (Worcester Polytechnic Institute);Elke Rundensteiner (Worcester Polytechnic Institute);Cao Lei (Massachusetts Institute of Technology)
There is a growing need for computing-supported methods that facilitate the automated signaling of Adverse Drug Reactions (ADRs) otherwise left undiscovered from the exploding amount of ADR reports filed by patients, medical professionals and drug manufacturers. In this research, we design a Multi-Drug Adverse Reaction Analytics Strategy, called MARAS, to signal severe unknown ADRs triggered by the usage of a combination of drugs, also known as Multi-Drug Adverse Reactions (MDAR). First, MARAS features an efficient signal generation algorithm based on association rule learning that extracts non-spurious MDAR associations. Second, MARAS incorporates contextual information to detect drug combinations that are strongly associated with a set of ADRs. It groups related associations into Contextual Association Clusters (CACs) that then avail contextual information to evaluate the significance of the discovered MDAR Associations. Lastly, we use this contextual significance to rank discoveries by their notion of interestingness to signal the most compelling MDARs. To demonstrate the utility of MARAS, it is compared with state-of-the-art techniques and evaluated via case studies on datasets collected by U.S. Food and Drug Administration Adverse Event Reporting System (FAERS).