2020 January JAMIA Journal Club Webinar

January 9, 2020
3:00PM
4:00PM
EDT
Fee: 
Free for AMIA members; $50 for non-members.
Presenters: 
Chao Yan - PhD Candidate

Ensuring electronic medical record simulation through better training, modeling, and evaluation.

Article co-author Chao Yan will discuss this month's JAMIA Journal Club selection:

Zhang Z, Yan C, Mesa DA, Sun J, Malin BA. Ensuring electronic medical record simulation through better training, modeling, and evaluation. J Am Med Inform Assoc. 2020 Jan 1;27(1):99-108. doi: 10.1093/jamia/ocz161. [Abstract]
 

Presenter

Chao Yan
PhD Candidate
Department of Electrical Engineering and Computer Science at Vanderbilt University
Nashville, TN

Chao Yan is a Ph.D. student at the Department of Electrical Engineering and Computer Science at Vanderbilt University in Nashville, Tennessee. He is a member of the Health Information Privacy Laboratory, which is a part of the Vanderbilt Health Data Science Center. His research focuses on 1) the mechanisms of anomaly detection/auditing in EHR access logs for catching/deterring malicious insiders, 2) EHR simulation via generative models, and 3) representation learning and predictive modeling in the healthcare domain.   

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

Travis R. Goodwin, PhD, Research Fellow, Lister Hill National Center for Biomedical Communications (LHNCBC), U.S. National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD

 



Citation

The PubMed citation for the paper under discussion is:

Zhang Z, Yan C, Mesa DA, Sun J, Malin BA. Ensuring electronic medical record simulation through better training, modeling, and evaluation. J Am Med Inform Assoc. 2020 Jan 1;27(1):99-108. doi: 10.1093/jamia/ocz161. [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

Electronic medical records (EMRs) can support medical research and discovery, but privacy risks limit the sharing of such data on a wide scale. Various approaches have been developed to mitigate risk, including record simulation via generative adversarial networks (GANs). While showing promise in certain application domains, GANs lack a principled approach for EMR data that induces subpar simulation. In our work, we improve EMR simulation through a novel pipeline that (1) enhances the learning model of GANs, (2) incorporates evaluation criteria for data utility that informs learning, and (3) refines the training process. We evaluated the new and existing GANs with utility and privacy measures using billing codes from over 1 million EMRs at Vanderbilt University Medical Center and verify the effectiveness of our work.

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:

  • Comprehend the manner by which generative adversarial networks (GANs) work to simulate data relevant to electronic medical records (EMR)
  • Distinguish between the differences in GAN architectures designed for the healthcare domain
  • Describe utility and privacy evaluation measures for synthetic EMRs, and
  • Identify conditional training strategies to inform the EMR generation for low-prevalence clinical concepts.
     

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: Chao Yan
JAMIA Journal Club planners: Michael Chiang, Travis Goodwin, Maryam Zolnoori
AMIA staff: Susanne Arnold, Pesha Rubinstein

Contact Info

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