OBJECTIVE: With the intensifying global population aging, the demand for mechanical ventilation in geriatric patients is rising. Given their complex physiological traits and sparse intensive care unit (ICU) data, accurate intubation prediction is difficult. Premature intubation may raise the risk of hypoxic organ damage, whereas delayed intubation can lead to increased ventilator-associated mortality. Therefore, developing precise intubation prediction models is vital for elderly ICU patients. METHODS: This study retrospectively analyzed data from ICU patients aged over 65 years in the MIMIC-IV and eICU databases. The intubation prediction task was formulated using a sliding window with a strict temporal data split to avoid data leakage. We propose a dynamic mask attention graph neural network (DymaGNN) to capture the time-varying relationship of key physiological variables by constructing a dynamic heterogeneous graph structure and an adaptive edge-weighting mechanism. The mask attention layer is designed to identify the key timesteps in the irregular sampling data. RESULTS: The experiments showed that DymaGNN achieved an area under the curve (AUC) value of 0.8363 and 0.8557 on the intubation prediction task on MIMIC-IV and eICU datasets, respectively, and maintained an AUC of 0.8115 under a 15% data missing rate. Visualization of the feature interaction graph revealed the relationship between important features such as respiratory rate and oxygen saturation. These interaction patterns matched much clinical knowledge, significantly improving doctors' trust in the model prediction. CONCLUSION: 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.