October JAMIA Journal Club Webinar

October 12, 2017
Free for AMIA members and students of Academic Forum member institutions; Others: $50
Tian Kang, MA

EliIE: An open-source information extraction system for clinical trial eligibility criteria

Author Tian Kang will discuss this month's JAMIA Journal Club selection:

Kang T, Zhang S, Tang Y, Hruby GW, Rusanov A, Elhadad N, Weng C. EliIE: An open-source information extraction system for clinical trial eligibility criteria. J Am Med Inform Assoc. 2017 April 1. 

https://www.ncbi.nlm.nih.gov/pubmed/28379377 (link to abstract) 


Tian Kang, MA
PhD Student
Columbia University Department of Biomedical Informatics
New York, NY

Tian Kang is a PhD student in the Department of Biomedical Informatics (DBMI) at Columbia University, where she earned her Masters degree. Her advisor is Dr. Chunhua Weng.
Her main research interest is clinical Natural Language Processing. She primarily works on applying NLP techniques and Machine Learning methods to unstructured data to help improve the quality of healthcare and clinical research, including clinical notes, clinical research text and health consumer-generated text. She earned her BS in Bioinformatics at Huazhong University of Science and Technology in China, and worked as a bioinformatics engineer at the Beijing Genomics Institute in the area of immune repertoire.


  • 40-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.
  • 20-minute discussion of questions submitted by listeners via the webinar tools.


  • Follow @AMIAinformatics and #JAMIAJC for Journal Club information.
  • Participants also receive short feedback surveys to evaluate the JAMIA JC webinar.


JAMIA Journal Club managers are JAMIA Student Editorial Board members:

Lucy Lu Wang, PhD Candidate, Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 

Jingcheng Du, Pre-doctoral Research Fellow and PhD Student, Ontology Research Group, University of Texas Health Science Center at Houston School of Biomedical Informatics, Houston, TX


The PubMed citation for the paper under discussion is:

Kang T, Zhang S, Tang Y, Hruby GW, Rusanov A, Elhadad N, Weng C. EliIE: An open-source information extraction system for clinical trial eligibility criteria. J Am Med Inform Assoc. 2017 April 1. 

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 trial-eligible patients in the electronic health record has been a holy grail for the biomedical informatics research community for nearly three decades. The free-text format of eligibility criteria (EC) and lack of standardization challenge EHR extraction for optimized cohort selection, large-scale aggregative analytics, and collaborative clinical research. Current formalizations require laborious manual interpretation of the syntactic rules and semantic concepts in EC, and largely lack semantic interoperability with EHR data.

Clinical researchers are eager for an automated information extraction system that can parse all free-text EC text to structured EHR data queries. Such an automated system would help researchers overcome the difficulties in identifying patients who fit predetermined eligibility criteria, and, with a query designed for EHR interoperability, help researchers cast a wider net in patient recruitment and enrollment. 

In the October JAMIA Journal Club, lead author Kang discusses Kang et al’s study on natural language processing using the OMOP Common Data Model, with Alzheimer’s disease—one of the most well-studied diseases in the US—to illustrate the methodology. 

Target Audience

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

Learning Objective

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

  • Consider using an open-source machine learning-based information extraction system for identifying eligible patients in the EHR for clinical trial recruitment and enrollment
  • Recognize the role of OMOP Common Data Model in improving the interoperability between clinical research data and EHR data.


Tian Kang, MA
PhD Student
Columbia University Department of Biomedical Informatics
New York, NY

Accreditation Statement

The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Credit Designation Statement

The American Medical Informatics Association designates this live activity for a maximum of 1 AMA PRA Category 1 Credit(s). Physicians should claim only the credit commensurate with the extent of their participation in the activity. 

Criteria for Successful Completion

Completion of this live activity is demonstrated by:

  • Viewing the live webinar
  • Optional submission of questions via webinar feature; option to follow @AMIAinformatics and tweet via #JAMIAJC
  • Completion of the evaluation survey emailed at the webinar's conclusion, and 
  • Verification of attendance through the participant's electronic report through the individual login to AMIA Central at www.amia.org. 

The physician participant will be able to generate a CME certificate through the AMIA automated system. 
For a certificate of completion, contact Pesha@amia.org.

Commercial Support

No commercial support was received for this activity.

Disclosure Policy

As a provider accredited by the ACCME, AMIA requires that everyone who is in a position to control the content of an educational activity disclose all relevant financial relationships with any commercial interest for 12 months prior to the educational activity.

The ACCME considers relationships of the person involved in the CME activity to include financial relationships of a spouse or partner.

Faculty and planners who refuse to disclose relevant financial relationships will be disqualified from participating in the CME activity. For an individual with no relevant financial relationship(s), the participants must be informed that no conflicts of interest or financial relationship(s) exist.

AMIA uses a number of methods to resolve potential conflicts of interest, including: limiting content of the presentation to that which has been reviewed by one or more peer reviewers; ensuring that all scientific research referred to conforms to generally accepted standards of experimental design, data collection, and analysis; undertaking review of the educational activity by a content reviewer to evaluate for potential bias, balance in presentation, evidence-based content or other indicators of integrity, and absence of bias; monitoring the educational activity to evaluate for commercial bias in the presentation; and/or reviewing participant feedback to evaluate for commercial bias in the activity.

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 Faculty: Tian Kang
JAMIA Journal Club planners: Jingcheng Du, Lucy Lu Wang
AMIA staff: Susanne Arnold, Pesha Rubinstein

The following provide their disclosures of relevant financial relationships with commercial interests that have occurred within the previous 12 months:

JAMIA Student Editorial Board Advisor Michael Chiang:
Grants/Research Support: NIH, NSF
Consultant: Novartis; Clarity Medical Systems (unpaid member of Scientific Advisory Board)

Instructions for Claiming CME/CE Credit

CME site (MyAMIA) works best with IE 8 or above version, Chrome, Safari, and Firefox.

  • Login to your account at amia.org; in upper right hand corner, click on AMIA Central
  • Go to “My Events" under Membership/Activities
  • Click “Apply for Credits" for this webinar
  • Follow the instructions on the Credit Registration page. Be sure both drop-down menus say “physician”
  • To print out your certificate, go to "My CME/CE Credits" under Membership/Activities.
  • Physicians will be able to print out or save their CME certificates.
  • Other attendees: if you require a certificate of participation, please contact pesha@amia.org

Contact Info

For questions about content or CE, email pesha@amia.org.