Applied Math Seminar: Making Sense of Pharmacovigilance and Drug Adverse Event Reporting Using Machine Learning
Speaker: Xuan Xu (K-State)
Abstract: Drug-associated adverse events cause approximately 30 billion dollars a year of added health care expenses, along with negative health outcomes including patient death. This constitutes a major public health concern. The US food and Drug Administration (FDA) requires drug labeling to include potential adverse effects for each newly developed drug product. In this study, we make the most of the recently disseminated ADE data by the FDA for animal drugs and devices used in animals to address this public and welfare concern. For this purpose, we implemented different similarity methods to determine the most efficient and robust approach to properly discover highly associated ADEs from the reported data and accurately exclude noise-induced reported events, while maintaining a high level of correlation precision.
Friday, November 15 at 3:30pm to 4:20pm