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We are happy to announce our winning groups for AI Health Data Challenge 2020:


1st Prize: SMART App on FHIR (David Chun, Dept of ECE, Patrick Chao, School of Information, Joel Wang, Dept of ECE, UT Austin)

2nd Prize: i-Radiodiagno: Clinically Accurate Report Generation (Nandhini Lakuduva, Dept of Computer Science, Josué Alfaro, Dept of Computer Science, UT Austin)

It is our goal to bridge disciplines and promote data-driven, evidence-based care. We are looking for participants from diverse backgrounds with skills in information science, computer science, and life sciences, to leverage their collective knowledge as a team and create an app or a data analytics tool/package for mining large datasets of electronic medical records. The app or tool should produce valuable knowledge to support clinical decision making and present it through an effortless and immediate interface.

Why should you care ?

Healthcare is now the US’s No.1 employer with 1 in 8 americans working in this sector. The U.S. Bureau of Labor Statistics states that there are 16 million medical-related jobs out there and the profits this sector generates reaches to $2.7 trillion a year. Solving burning issues (e.g., improving quality of care, delivering care intelligently, and reducing cost by using AI technologies) can have an immense impact in this country and worldwide.


Solving Urgent Problems in Healthcare

Feedback Package for All Participants


Everyone is busy, so spending time wisely is critical. Joining this event can bring you the following benefits:

The Power of Data and Design


The winning innovations in industry are based on the combination of data and design (e.g., Apple, Tesla, and more). Unfortunately data and design are divided into distant groups. This challenge provides a unique chance to merge these distant groups so that you can team up together to uncover the magic of data and design.

A Strong Resume


This challenge is based on real-world large scale datasets in healthcare. It will provide you hands-on experience to better understand issues in healthcare and how AI can help

Meeting Peers


During this challenge, we encourage participants to work in teams to truly bring data and design together. So, you will meet like-minded friends and creative souls. We use slack to boost the communication and help you form teams.



On our judging day (April 3, 2020), you will meet our judges who are leaders and experts in academia and industry. You will also meet other teams participating our challenge. It will be a great networking event.

MIMIC Datasets

This challenge is based on MIMIC datasets. Currently MIMIC has three datasets available. Since the datasets have to be treated with appropriate care and respect, researchers seeking to use these datasets must formally request access. Access is granted for an individual person. So even if you are working in a team, each team member needs to get the separate permission.

Prior to requesting access to MIMIC, you will need to complete the CITI online course “Data or Specimens Only Research”. It usually takes 1-2 weeks to get permission.

Please plan accordingly.

MIMIC is an openly available dataset developed by the MIT Lab for Computational Physiology, comprising deidentified health data associated with ~60,000 intensive care unit admissions.

It includes demographics, vital signs, laboratory tests, medications, and more.

How to Request Access

The eICU Collaborative Research Database is a large multi-center critical care database made available by Philips Healthcare in partnership with the MIT Laboratory for Computational Physiology.

How to Request Access

The MIMIC Chest X-ray (MIMIC-CXR) Database v1.0.0 is a large publicly available dataset of chest radiographs with structured labels. The dataset contains 371,920 images corresponding to 224,548 radiographic studies performed at the Beth Israel Deaconess Medical Center in Boston, MA.

The dataset is de-identified to satisfy the US Health Insurance Portability and Accountability Act of 1996 (HIPAA) Safe Harbor requirements. Protected health information (PHI) has been removed.

The dataset is intended to support a wide body of research in medicine including image understanding, natural language processing, and decision support.

(Notes: once you get the permission for MIMIC ICU dataset, you can use the same permission to download MIMIC-CXR dataset).

How to Request Access


No teammate(s)? No worries! You can join our Slack channel to find team members who have registered for the same. Click beneath button to request an invite to the Slack channel.You can also use this to contact us regarding any questions about the challenge.

*  In case you have formed a team amongst other participants already, only one member per team needs to fill out this form.You will have a chance to fill out the details of other team member(s) on the next section of this form.

Build Team Now

You Still Need to Know...


We made the new deadlines (due to the COVID-19 Pandemic):

  • Register by  Jan 31, 2020
  • Download dataset no later than Feb 15, 2020
  • Final submission due by 11pm CST on May 14, 2020
  • Announcement of finalists by May 20, 2020
  • Presenting, judging, and announcing final winners: May 26, 2020

Final Deliverables

Please submit the following items before 11PM Central Time May 14, 2020 to this UT Box folder (Note: submit only one zip file containing everything and filename as the submitter’s full name)

  • App/tool: the link to your GitHub
  • A 2-5 page report on the details of how you built the app. This report should contain the details of your methods, and screenshots of your app/tool.
  • 5-minute video about your process

How to win

The judging criteria are:

  • Usability: Is your app/tool useful to address some healthcare issues? Is your app/tool easy to use?
  • Novelty: What are NEW features in your app/tool? Are there any creative and exciting things in your app/tool?
  • Reproducibility: Can others reproduce your app/tool?

Our Sponsors

Suit Endowment Fund & Mary R. Boyvey Dean’s Excellence Fund