On October 18, 2019, the DII Challenge Workshop was hosted by the UTHealth School of Biomedical Informatics as the culminating event of the 2019 DII Challenge, SBMI’s first national data science competition. For more information on the DII Challenge Workshop, click to view the SBMI's 2019 DII Challenge news story. Below, see some of the highlights of this event; for more photos, click to view the DII Challenge Album .
Task 1, Sepsis Onset Prediction: GuanLab Team
Yuanfang Guan and Xianghao Chen* – University of Michigan
Task 2, Sepsis Mortality Prediction: NCH Team
Simon Lin*, Xianlong Zeng*, Steve Rust, and Sven Bambach – Nationwide Children’s Hospital
Task 3, Innovation Track: LCP Team
Tom Pollard* and Alistair Johnson* – MIT
Honorable Mention: Buckeye AI Team
Ping Zhang, Changchang Yin*, and Dongdong Zhang* – The Ohio State University
…And Congratulations to All of our Participating DII Challenge Teams!!
Developing advanced AI/machine learning techniques to leverage large, diverse sets of health data (e.g., derived from electronic health records, claims, social determinants of health, and devices) represents an extraordinary means for potentially improving patient safety and quality of care through groundbreaking solutions to fundamental questions that can only be derived through the effective management and analysis of big data.
To this end, UTHealth School of Biomedical Informatics is hosting The 2019 DII National Data Science Challenge to enable participating teams to leverage a subset of the de-identified, EHR-derived Cerner Health Facts® database for the purpose of solving a clinically relevant problem to advance human health.
The specific use case and related tasks leveraging Cerner Health Facts® have been determined by SBMI, in collaboration with its sponsor, Cerner Corporation. AWS is providing cloud support for the Challenge. In 2019 the Challenge use case is focused on predictive accuracy for two tasks (tracks): sepsis onset and sepsis mortality. The most innovative solution (not necessarily the one with best performance) to the above referenced tasks constitutes a third track for the Challenge.
Specifics regarding the use case and related tasks can be found here.
(A complete copy of the Rules and Conditions is incorporated into the Challenge material provided electronically as part of registration. A hard copy may also be requested via the Challenge e-mail address: DII.NDSC@uth.tmc.edu.)
Entry Submission Requirements
Data Use Agreement
For the purposes of the Challenge, the Participant will be granted access to a Cerner Health Facts® dataset extract (the “Challenge Dataset”) made available through a secure infrastructure. As a condition of participation in the Challenge and prior to receiving access to the Challenge Dataset, Participant must to sign and comply with the terms of the Challenge Participant Data Use Agreement with Cerner (provided electronically, as part of the registration process).
The Host will select a panel of Judges to validate and score the submissions, and their decisions are final and binding. The Judges will review all eligible submissions received and select three winners based on the following aspects of the Challenge:
The following prizes will be awarded by the Host/Sponsor to the winning Challenge Participants:
Winning Participants are responsible for all applicable taxes, required reporting related to any prize received as part of the Challenge, and the distribution of prize money among team members.
Privacy and Participant Data
Participant agrees that the Host may collect, store, share, and otherwise use personally identifiable information provided during the registration process and throughout the Challenge for the purposes of general communication and promotion, as well as to convey additional opportunities to the Participant.
The Challenge Team Member Specifics and Institutional Support Form can be downloaded here.
For each registered team entering the Challenge, a secure link containing credential information will be provided, allowing team members to log into our reserved AWS instances. This credential may be shared among team members, but cannot be disclosed to any person who is not a member of the team. The login will be a two-step process: You will log into the prescribed server and make a subsequent connection to the assigned AWS virtual machine.
Note that each team is allocated a budget of $1,000 in AWS credits for the purposes of the Challenge. Initially, each team will have a small GPU instance p2.xlarge ($0.9/hour) to become familiar with the data. The team can build an initial solution on this instance and then request a one-time switch to p3.2xlarge ($3.06/hour) to speed up their training and scale-up their model for the final stage of the competition. Please send an email to DII.NDSC@uth.tmc.edu in order to make the request to switch. If there are any questions, we recommend that you to refer to the FAQs accessible through the Challenge website; if your question has not been addressed, please send us your query via the Challenge e-mail address, DII.NDSC@uth.tmc.edu, so that we can provide you a response and update the FAQs section.