Challenge
From Data to Decisions:
Predicting the Future of Trauma Care
Year Three: Putting Predictive Algorithms to the Ultimate Test
Over the past two years, teams from the Data Competition have used data from over 6,000 trauma center cases to train their predictive algorithms, developing robust predictions for need of life-saving interventions (LSIs).
In the third year, we are challenging teams with the following tasks:
- First look: Using the first five minutes of pre-hospital clinical procedures to make LSI predictions that may be needed hours later.
- Continuous triage: As more in-hospital clinical data is presented, accurately predict LSIs needed in the next 20 minutes.
- Resource prioritization: Scale LSI recommendations based on the availability of medical resources. This capability can help resource allocation planning in very large mass casualty incidents or in austere battlefield environments.
Teams
Qualified funded and unfunded teams participated in the competitions, striving to advance the use of autonomous systems for rapid, stand-off casualty assessment to facilitate timely and accurate medical triage.
AI TEMPO* | Michigan Technological University |
AID3d | Seraphim Triage, USA |
AUSTERE | University of Vermont |
Coordinated Robotics* | Coordinated Robotics, USA Arete, USA |
CRAI | University of South-Eastern Norway, Norway |
DBbun | Dbbun, USA |
Disruptors | Independent Researchers, India |
Engineering Dynamics, USA | Engineering Dynamics, USA |
EverlignAI, USA | EverlignAI, USA |
Kri Labs | Massachusetts Institute of Technology |
Maxentric | MaXentric, USA |
MSAI* | MarvsAI, USA |
OCA-SENTINEL | Duke University |
SunnyGoose | University of Toronto, Canada |
Synthesis | Independent Researchers, USA |
Triage360 | Omedus, USA |
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DataFinals | 2026 | |
| Workshop* | March 27 |
| Qualifications* | June 28 - July 30 |
| Competition* | Sept. 30 |
| Awards ceremony | Nov. 14 |
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Overview
