Özlem Uzuner, Ph.D.
Özlem Uzuner, PhD
Visiting Associate Professor of Biomedical Informatics, Harvard Medical School

Ozlem Uzuner is an Associate Professor of Information Sciences and Technology at George Mason University. She is the lead investigator of the National NLP Clinical Challenges (n2c2) which she has been organizing since 2006, first under Informatics for Integrating Biology and the Bedside (i2b2), then under CEGS N-GRID, and now under the Department of Biomedical Informatics (DBMI) of Harvard Medical School jointly with George Mason University. She is a natural language processing (NLP) expert and specializes in methods that process clinical narratives for effective and efficient information access that can support clinical applications. As a part of her NLP efforts, she de-identifies clinical records; as a part of the outreach and education efforts of DBMI and George Mason, enables use of de-identified narrative clinical data for research and course work.

Emerging clinical applications of text analytics.
Authors: Spasic I, Uzuner Ö, Zhou L.
Int J Med Inform
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Normalizing Adverse Events using Recurrent Neural Networks with Attention.
Authors: Lee K, Uzuner Ö.
AMIA Jt Summits Transl Sci Proc
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Advancing the state of the art in automatic extraction of adverse drug events from narratives.
Authors: Uzuner Ö, Stubbs A, Lenert L.
J Am Med Inform Assoc
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Cohort selection for clinical trials: n2c2 2018 shared task track 1.
Authors: Stubbs A, Filannino M, Soysal E, Henry S, Uzuner Ö.
J Am Med Inform Assoc
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New approaches to cohort selection.
Authors: Stubbs A, Uzuner Ö.
J Am Med Inform Assoc
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An Exploratory Study on Pseudo-Data Generation in Prescription and Adverse Drug Reaction Extraction.
Authors: Tao C, Lee K, Filannino M, Uzuner Ö.
Stud Health Technol Inform
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An Empirical Test of GRUs and Deep Contextualized Word Representations on De-Identification.
Authors: Lee K, Filannino M, Uzuner Ö.
Stud Health Technol Inform
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Corrigendum to "Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 CEGS N-GRID shared tasks Track 2" [J Biomed Inform. 2017 Nov;75S:S62-S70].
Authors: Filannino M, Stubbs A, Uzuner Ö.
J Biomed Inform
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Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks.
Authors: Filannino M, Uzuner Ö.
Yearb Med Inform
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FABLE: A Semi-Supervised Prescription Information Extraction System.
Authors: Tao C, Filannino M, Uzuner Ö.
AMIA Annu Symp Proc
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