The Center for Drug Evaluation and Research (CDER) within the U.S. Food and Drug Administration (FDA) is responsible for ensuring the marketing of safe and effective drugs in the United States. It also regulates prescription drugs and over-the-counter drugs. Judith Lamont, a research analyst and a senior writer with KMWorld, highlights how CDER uses statistical modeling and text analytics to achieve this goal.
CDER evaluates the results of clinical trials to determine whether certain drugs should be allowed on the market. It monitors these drugs post-market through tracking adverse events. Post-market adverse event reports are made up of both structured and unstructured data that come from numerous sources, including physicians, pharmacists, and public health supervisors. As the volume of information is high, CDER uses statistical modeling and text analytics to find patterns and associations and interpret the information quickly.
CDER uses statistical tools and text analytics to capture insights that reflect the occurrences of major adverse events. If one event is dominating or there is an association between events, then the key items are extracted from these adverse events by leveraging text mining. In addition, text mining is used to present a variety of visualizations to clarify the insights that result from the analyses. Furthermore, the expertise of domain experts from medical surveillance is leveraged for building the models so that the models reflect real-world problems and not just a statistical model.
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