Drug-drug interaction related adverse events (DIAE) signals are a major public health issue. Drug safety analysts must sift through thousands of adverse event reports submitted daily to U.S. Food and Drug Administration (FDA) to discover unexpected DIAE signals, which if addressed can lead to life-saving actions. To facilitate the DIAE discovery from these massive data sets, we design several technological innovations that together are integrated into an interactive visual analytics system called DEVES 1. First, our state- of-the-art DIAE mining algorithm efficiently infers potential DIAE signals from these reports, and then ranks them based on their significance score. For interpretability of these inferred DIAE signals, domain knowledge of adverse events and already known drug interactions is extracted from external authoritative data sources and then seamlessly integrated with the inferred signal set. Guided by this augmented signal model, DEVES supports advanced signal analytics - empowering the analyst to interact with linked visualizations offering complementary perspectives into the signal set and its supporting evidence in the form of reports. Our demonstration showcases the technological innovations of DEVES using real-world FDA datasets, demonstrating that DEVES effectively supports the core regulatory tasks from signal screening, signal prioritization to signal validation.

Adverse drug reactions (ADRs) caused by drug-drug interactions (DDI) are a major cause of morbidity and mortality worldwide. There is a growing need for computing-supported methods that facilitate the automated signaling of DDI related ADRs (DIARs) that otherwise would remain undiscovered in millions of ADR reports. In this demonstration, we showcase our MeDIAR technology - an end-to-end DIAR signal generation, exploration and validation solution for pharmaceutical regulatory agencies to detect true DIAR signals from a drug surveillance database. MeDIAR's innovations include an efficient rule-driven learning algorithm for deriving DIAR signals from ADR reports, an innovative scoring methodology based on the proposed contextual association cluster model to rank the generated signals by their importance. Further, these ranked signals are augmented with meta information such as their significance level and their severity, along with links to their supporting ADR reports. Lastly, MeDIAR features an interactive visual analytics interface to support drug safety evaluators in reviewing and discovering unknown severe DIARs.