Intelligent monitoring of patient vital signs based on adaptive attention fusion spatiotemporal graph neural network

This study proposes a vital signs monitoring framework that addresses the limitations of traditional threshold-based alarms and existing deep-learning models in capturing multimodal physiological interactions and spatiotemporal dynamics.The method integrates an adaptive attention fusion mechanism that dynamically adjusts the importance of heterogeneous physiological parameters, a spatiotemporal graph neural network that jointly models inter-parameter correlations and temporal evolution using multi-scale windows, and a reinforcement learning module that enables active, strategy-driven early warning and clinical decision support.Evaluated on the MIMIC-III and eICU datasets, the proposed system achieves 96.3% anomaly detection accuracy, 38.5-minute early warning capability, and a 0.912 F1-score, outperforming existing methods.Ablation studies confirm the contributions of adaptive fusion, spatiotemporal graph modelling and policy optimisation.