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Put AI to Work
April 15-18, 2019
New York, NY
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Tom Sabo

Tom Sabo
Principal Solutions Architect, SAS


Tom Sabo is a Principal Solutions Architect with SAS who, since 2005, has been immersed in text analytics and artificial intelligence applied to federal government challenges. He presents work internationally on diverse topics including modeling applied to government procurement, strategies to counter human trafficking, and using analytics to leverage and predict research trends. Sabo also served on a panel for the Institute of Medicine’s Standing Committee on Health Threats Resilience to inform DHS/OHA on social media strategies. He has a bachelor’s degree in cognitive science and a master’s in computer science, both from the University of Virginia.


4:05pm4:45pm Wednesday, April 17, 2019
Case Studies, Machine Learning
Location: Sutton South
Secondary topics:  AI case studies, Health and Medicine, Models and Methods, Text, Language, and Speech
Tom Sabo (SAS), Qais Hatim (Center for Drug Evaluation and Research, U.S. Food and Drug Administration)
Drug adverse event narratives contain a wealth of information that is laborious to assess using manual methods. To improve FDA Pharmacovigilance, we apply rule-based text extraction to generate training data for deep learning models. These models improve the identification of adverse events from narrative data, enhance time-to-value, and refine sources of medical terminology. Read more.