2019 December JAMIA Journal Club Webinar

December 12, 2019
3:00PM
4:00PM
EDT
Fee: 
Free for AMIA members; $50 for non-members.
Presenters: 
Fenia Christopoulou - PhD Candidate

Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods

Lead author Fenia Christopoulou will discuss this month's JAMIA Journal Club selection:

Christopoulou F, Tran TT, Sahu SK, Miwa M, Ananiadou S. Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods. J Am Med Inform Assoc. 2019 Aug 7. pii: ocz101. doi: 10.1093/jamia/ocz101. [Epub ahead of print] [Abstract]
 

Presenter

Fenia Christopoulou
PhD Candidate
The University of Manchester
United Kingdom

Fenia Christopoulou is a final-year PhD student at the University of Manchester, School of Computer Science, National Centre for Text Mining, in the UK. Her PhD topic focuses on the development of graph-based neural architectures for relation extraction within and across sentences.  

Format

  • 35-minute discussion between the authors and the JAMIA Student Editorial Board moderators including salient features of the published study and its potential impact on practice.
  • 25-minute discussion of questions submitted by listeners via the webinar tools and moderated by JAMIA Student Editorial Board members
JAMIA Journal Club managers and monthly moderators are JAMIA Student Editorial Board members:

Manager

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


Moderator

Sally L. Baxter, MD, MSc, Postdoctoral Scholar, UCSD Health Department of Biomedical Informatics, UCSD Shiley Eye Institute and Viterbi Family Department of Ophthalmology; Physician, Veterans Affairs San Diego Healthcare System, La Jolla, CA 

 


Citation

The PubMed citation for the paper under discussion is:

Christopoulou F, Tran TT, Sahu SK, Miwa M, Ananiadou S. Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods. J Am Med Inform Assoc. 2019 Aug 7. pii: ocz101. doi: 10.1093/jamia/ocz101. [Epub ahead of print] [Abstract]

Student Access

Students who are not AMIA members, but whose academic institutions are members of the Academic Forum, are eligible for a complimentary JAMIA Journal Club registration. Please contact Susanne Arnold at susanne@amia.org for the discount code. In the email, please include: full name, Academic Department, and the primary Academic Forum representative of that Academic Department. Note that AMIA Student memberships are $50, which allow access to JAMIA, all JAMIA Journal Clubs, and other webinars of interest to the biomedical informatics community. 

Statement of Purpose

Automatic identification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events (ADEs). Existing methods for automatic extraction of Drug-Medication interactions typically incorporate external syntactic tools, external resources, as well as hand-crafted features. Neural architectures have achieved state-of-the-art results on various relation extraction tasks without requiring feature engineering.

In this study, the researchers propose an ensemble of neural models for automatic identification of interactions between Drugs and Medication-related entities, including ADEs, as part of their participation to the n2c2 2018 challenge. They propose a generalizable system that is independent of domain-specific tools and is able to detect both associations inside and across sentences. They additionally show that incorporation of additional Drug-Drug Interactions (DDIs) into the network learning can further improve detection of Drug-Medication associations. 

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:

  • Have a clearer understanding of the relation extraction task in electronic health records
  • Develop neural models for relation extraction between biomedical entities

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: Fenia Christopoulou
JAMIA Journal Club planners: Michael Chiang, Maryam Zolnoori
AMIA staff: Susanne Arnold, Pesha Rubinstein

JAMIA Journal Club planner Sally L. Baxter discloses that she has received grant/research support from the Heed Ophthalmic Foundation and from NIH NLM Training Grant T15LM011271. 

Contact Info

For questions about webinar access, email Susanne@amia.org.