DRAGON’s simple website can help speed up the use of machine learning tools to improve COVID care
Around the world, doctors are using artificial intelligence to help them make decisions about patient care during the COVID crisis. Machine learning models, feeding on clinical, laboratory, genetic, and radiological datasets, are able to churn out predictions that can be used to identify, for example, people who ought to self-quarantine, others who ought to make their way to the hospital, and others still who might need to be admitted to intensive care. Even questions about who is most in need of access to sometimes scarce vaccines can be guided by computational modelling. Beyond improving care, they can also help ease the burden on stretched hospital resources.
With more and more of these models popping up around the globe, the DRAGON project set out to create an online platform that would serve as an open source repository for a curated subset, with a simple interface that allows users to make online calculations. The website can be used by doctors to supplement their judgment with patient-specific predictions from externally validated models in a user-friendly format.
DRAGON sought out publicly available, validated, peer reviewed or open-source models and published them alongside supporting documentation and links to associated articles. The platform is dynamic and growing; it currently features nine models, and will continue to be populated with others as they become available.
It is hoped that the platform will help speed up the adoption of predictive models, moving them from the research world into clinical practice. Researchers working on COVID19 modelling can get in touch with the DRAGON project team to request that their model be included on the website, thereby increasing the reach and impact of their work.