Methodological development study: Dynamic mask attention graph neural network for mechanical ventilation in elderly intensive care unit patients

Our proposed DymaGNN establishes a useful method for mechanical ventilation prediction in elderly ICU patients, achieving high predictive accuracy and remaining robust under a 10% data missing rate. Its interpretable feature interaction graphs provide transparent insights, aligning with established medical knowledge to build trustworthy tools for real-world ICU intubation decisions.