CARIS is a new agentic LLM framework that automates clinical research workflows from planning to reporting in a coding-free and privacy-preserving manner, achieving high completeness scores on heterogeneous datasets.
Develop- ment of interpretable machine learning mod- els for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study
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Coding-Free and Privacy-Preserving Agentic Framework for Data-Driven Clinical Research
CARIS is a new agentic LLM framework that automates clinical research workflows from planning to reporting in a coding-free and privacy-preserving manner, achieving high completeness scores on heterogeneous datasets.