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Put AI to Work
April 15-18, 2019
New York, NY
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Qais Hatim

Qais Hatim
Computer Scientist, Center for Drug Evaluation and Research, U.S. Food and Drug Administration


Qais received a dual Ph.D. degrees in operation research and industrial engineering from Pennsylvania State University/University Park in August 2015. In his role as computer scientist/statistician at FDA, he conducts research in statistical/operational modeling and computer science at Center of Drug Evaluation and Research (CDER)/ Office of Translational Science (OTS)/ Office of Computational Science (OCS) in the U.S. Food and Drug Administration (FDA). Specifically, he applies advanced statistical modeling and scientific computing techniques to computationally intensive tasks that are encountered in regulatory and scientific applications. For this purpose, he utilizes various statistical and operations research methodologies such as machine learning and data mining algorithms, natural language processing (NLP) techniques, Neural Networks procedures, and text analytics to extract meaning, patterns and hidden structures in structured and unstructured data; identifying the most feasible approaches to software/networking system design and development problems; consulting reviewers, fellow scientists, and regulations to analyze problems and recommend technology based solutions. He also prepares reports and manuscripts based on research findings and will present at scientific meetings as necessary. Moreover, he is an active member in several working groups across FDA such as the Modeling and Simulation Workgroup, INFORMED and HIVE.


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.