A brand-new challenge for artificial intelligence and machine learning developers, to build models that can predict heart failure-related health outcomes including stroke, for veterans has been launched by a collaboration between regulators and healthcare organisations in the UK and US.
The Veterans Cardiac Health and AI Model Predictions (V-CHAMPs) Challenge, has been launched by the VHA Innovation Ecosystem, the FDA Digital Health Center of Excellence, precisionFDA (part of the FDA’s Office of Digital Transformation) and the Medicines and Healthcare products Regulatory Agency (MHRA).
By using entirely synthetic datasets based on real US veteran health records, participants will explore a range of potential outcomes following heart failure, including the prediction of cardiovascular outcomes, hospital readmission rates and mortality.
It is hoped that this will help health systems both in the US and the UK to better understand the cardiovascular risks and specific health outcomes in veterans, as well as increasing understanding of synthetic data and AI/ML models and how they could be used to explore similar topics in the future.
In the US, cardiovascular diseases such as heart failure, heart attacks, strokes, and peripheral artery disease are the leading cause of hospitalisation in the Veterans Affairs healthcare system and a major cause of disability impairing the lives of veterans across the United States.
Paul Campbell, interim Head of Software at the MHRA, says: “This is a unique chance for any developer or team to get involved, challenge themselves against other experts across the globe, all while contributing towards a worthy goal.
“Having access to hi-fidelity synthetic datasets will be a game-changer in this space, offering an opportunity for participants to test the potential of synthetic data to its limits.
“I’m so pleased to be able to work with our US partners, bringing together cutting-edge health data expertise from both sides of the Atlantic, and I can’t wait to see what the contestants build with this data.”
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