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Date
2015-09-23
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Adverse drug reactions (ADRs), a subset of the broader adverse events (AEs), have been shown in several studies to have a considerable burden on healthcare costs and patient outcomes. ADRs account for a significant increase in patient morbidity, mortality, and additional healthcare costs. In this presentation, we explore ADRs and AEs from the U.S. Food and Drug Administration's Adverse Event Reporting System (FAERS) data set. Using big data analysis tools from the Hadoop ecosystem, including Apache Spark, we analyze the FAERS data and discuss interesting trends and observations in the 10+ year historical data set.
Description
Dr. Nicholas Davis is Assistant Professor of Research in Medical Informatics at the University of Oklahoma-Tulsa School of Community Medicine. He received his BS in Computer Science with a minor in Mathematics, his MS in Computer Science with a focus in Information Security, and his PhD in Computer Science, all from the University of Tulsa (TU). For his doctoral work at TU, Dr. Davis performed research in bioinformatics, focusing on genomic analysis of immune response data sets and analysis of fMRI brain imaging data to identify regions of interest. In addition to his academic experience, Dr. Davis has accumulated over a decade of industry experience in a variety of technology roles, such as software development and architecture, network and system administration, and information security, including being a Certified Information Systems Security Professional (CISSP). He is inventor on a patent for Methods and Systems for Graphical Image Authentication.
His current projects include analysis of type 1 diabetes mellitus data to determine insulin pump settings correlated to improved glycemic outcomes, as well as data mining of clinical and claims data sets to understand and create predictive models of medication adherence across multiple dimensions. Dr. Davis's research interests include analysis of electronic health record and claims data, data science algorithms and tools, machine learning/statistical inference, diabetes, medication adherence, integrative analysis of heterogeneous biological data sets, and high performance computing.
Keywords
Health Sciences, Medicine and Surgery.