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Enhancing Clinical Concept Extraction with Contextual Embeddings

This on-demand webinar does not offer CE credit.

Si Y, Wang J, Xu H, Roberts K. Enhancing clinical concept extraction with contextual embeddings. J Am Med Inform Assoc. 2019 Jul 2. pii: ocz096. doi: 10.1093/jamia/ocz096.

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Kirk Roberts, PhD, MS
Assistant Professor
University of Texas Health Science Center at Houston
UTHealth School of Biomedical Informatics


Maryam Zolnoori, PhD,
Postdoctoral Research Fellow
Department of Digital Health Sciences and Department of Psychiatry and Psychology
Mayo Clinic


Tiffany J. Callahan, MPH,
PhD Candidate, Computational Bioscience Program
University of Colorado Denver Anschutz Medical Campus

Statement of Purpose

Neural network–based representations (or “embeddings”) have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more advanced embedding methods and representations (such as ELMo and BERT) have further pushed the state of the art in NLP, yet there are no common best practices for how to integrate these representations into clinical tasks. The purpose of this study, then, is to explore the space of possible options in utilizing these new models for clinical concept extraction, including comparing these to traditional word embedding methods (including word2vec, GloVe, and fastText).

Target Audience

The target audience for this activity is professionals and students interested in biomedical and health informatics.

Learning Objectives

The general learning objective for all of the JAMIA Journal Club webinars is that participants will

  • Use a critical appraisal process to assess article validity and to gauge article findings' relevance to practice

After this live activity, the participant should be better able to:

  • Explain how neural network-based embedding techniques are utilized in natural language processing (NLP)
  • Discuss how recent (“contextual”) embedding techniques have advanced NLP, including their potential impact on NLP for electronic health records
  • Identify key considerations for maximizing the impact of contextual embedding techniques for clinical NLP

This JAMIA Journal Club does not offer continuing education credit.

In our dedication to providing unbiased education even when no CE credit is associated with it, we provide planners’ and presenters’ disclosure of relevant financial relationships with commercial interests that has the potential to introduce bias in the presentation: 

Disclosures for this Activity

These faculty, planners, and staff who are in a position to control the content of this activity disclose that they and their life partners have no relevant financial relationships with commercial interests: 

JAMIA Journal Club presenter: Kirk Roberts
JAMIA Journal Club planners: Michael Chiang, Tiffany J. Callahan, Maryam Zolnoori
AMIA staff: Susanne Arnold, Pesha Rubinstein


Dates and Times: -
Type: Webinar
Course Format(s): On Demand