George B. Moody PhysioNet Challenges
For the past 24 years, PhysioNet and Computing in Cardiology have co-hosted a series of annual challenges, now called the George B. Moody PhysioNet Challenges, to tackle clinically interesting questions that are either unsolved or not well-solved.
The George B. Moody PhysioNet Challenge 2023 invites participants to predict outcomes for comatose patients in the hours following resuscitation from cardiac arrest.
We ask participants to design and implement working, open-source algorithms that, based only on the provided electroencephalogram (EEG) recordings and routine patient data, can predict good and poor outcomes for patients. The team with the best score for this task on the hidden test set wins the Challenge.
Please check the below links for information about current and past Challenges, including important details about scoring and test data for previous Challenges.
- June 6, 2023: The official phase of the George B. Moody PhysioNet Challenge 2023: Predicting Neurological Recovery from Coma After Cardiac Arrest has begun! We have greatly expanded the data, which now includes continuous EEG and ECG recordings. Please see our announcement on the Challenge forum for more details and submit your code when ready.
- April 12, 2023: We have launched a PLOS Digital Health Collection on Cost-effective point-of-care monitoring in low-resource settings. We encourage the 2022 Challenge participants and other researchers to submit their work to the collection. Please see the announcement page for more information.
- March 24, 2023: We are now accepting unofficial phase submissions to the 2023 Challenge. Please read the submissions instructions, double check your code, and submit your code when ready.
- March 1, 2023: The PhysioNet Challenges are a winner the NIH/FASB DataWorks! Prize.
- February 10, 2023: We launched the George B. Moody PhysioNet Challenge 2023: Predicting Neurological Recovery from Coma After Cardiac Arrest!
- July 8, 2022: We published an article, Rethinking Algorithm Performance Metrics for Artificial Intelligence in Diagnostic Medicine, in JAMA. This article explores the clinical utility of performance metrics for AI from the perspective of the Challenges.
- … see previous news articles here.
Supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number R01EB030362.