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Background
Innovative artificial intelligence (AI) methods and the increase in computational power support the use of tools and advanced technologies such as machine learning, which consumes large amounts of data to make predictions for actionable information.
Current AI workflows make it possible to conduct complex studies and uncover deeper insights than traditional analytical methods do. As the volume and availability of electronic health data increases, patient-centered outcomes research (PCOR) investigators need better tools to analyze data and interpret those outcomes. A foundation of high-quality training data is critical to developing robust machine-learning models. Training data sets are essential to train prediction models that use machine learning algorithms, to extract features most relevant to specified research goals, and to reveal meaningful associations.
Through this project, ONC in partnership with National Institutes of Health (NIH) National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), advanced the application of AI/ML in patient-centered outcomes research (PCOR) by generating high quality training datasets for a chronic kidney disease (CKD) use case – predicting mortality within the first 90 days of dialysis.
This project began in 2019 and ended in September 2021
See this blog post for more background.
The Office of the National Coordinator for Health Information Technology (ONC)